Artificial Life Experiments Show How Complex Functions Can Evolve

Yup. Essentially, atheists know that God exists but hate to admit it.

prove it.

Each time I say abiogenesis is impossible you argue with me.

For me to argue with you, you'd have to actually pay attention. I have yet to see your proof, or even your hint, as to why I should suppose prokariotes, which is what you are describing, could only have sprung up out of organic junk instantaneously.

Well, since you claim to have read the book, kindly tell us exactly what particles of matter can generate turing machines. This I need to see, I can use a good laugh.

Where did I claim that, exactly? Please show me before we go on--I'm pretty tired of you making stuff up that I've supposedly said and then putting on these supercelious tap-dancing shows when asked to produce the evidence from my own mouth.

In other words donh, I am asking you to back up your statement.

You have postulated that abiogenesis is categorically impossible. Yours the claim that needs backing up. My claim is quite a modest, but apposite one: that you haven't demonstrated compelling--even vaguely tempting, really--evidence, much less proof, of these ambitious universal claims.

btt

.In what manner is this a relevant response to the extract quoted?

Seems quite obvious to me that matter cannot reason

Which is, to repeat myself, since I was apparently ignored, relevant to what I said how?

No need to get angry. I'm only pointing out an inconsistency in your thinking. That doesn't translate to an attack or a threat or a put-down.

Let's get our emotional responses in order here. The problem is not anger, it is annoyance over what looks like obtuseness out of refusal to think about what is being read. As, for example, when you come right back with the same lazy, unresponsive misstatement of your deponents position, as you have just now done.

But the idea of evolution came after faith and religion. So the disproving is up to your side.

Apparently, you do not understand what the word "faith" means. "Faith" is the proud claim that you haven't proved diddly. Like science, I've no interest in the saturday night grudge match you've arranged between God and science. Science doesn't care about God, one way or another--God is not within science's domain of competence.

My argument against evolution is concicely stated in post#1265 just above.

So, apparently, you don't understand what "concise" means either. You have simply found an incredibly longwinded way to reject my suggestion that evolution did not always work the way it works in prokariotes. So it is just a restatement of your basic theme--that prokariotes had to leap into existence out of junk. This is the entire basis of your argument, absurd as it is, and you have simply window-dressed it to make it look more impressive than it is. It is likely that the rules governing meat machines are not going the be the rules for what went on before meat machines. My supposed contradiction is simply the fruit of me being willing to talk about pre- and post- meat machine paradigms--compounded by your inability to hone in on the precise details of any argument, busy as you are re-arranging your canned lecture yet again.

Pick and choose what you are arguing about and we can discuss that. Stop trying to purposely confuse the issues by saying that the argument against evolution does not apply to abiogenesis and that the argument against abiogenesis does not apply to evolution.

Take 5 seconds from your busy schedule to notice what I am actually arguing about regarding there being both a pre- and post- meat machine era, with distinct rules, and I'll consider discussing this further. Never mind agreeing or disagreeing with me--lets just see if you even understand.

freeper4u: "Programming a robot (or simulation of a robot) to walk in a real physical environment is hard problem"

AndrewC: "Don't you see the contradiction in your statement and how it relates to the rest of your statement?"

No.

No.

A simulation does not walk in a real physical environment. Programs are concepts as demonstrated in all of the virtual creatures that were "created" in the link you cited. IOW nothing that you presented as evidence was a material object.

Andrew,

In this example, the point I set out to make, and I think it was proved in the paper is that many problems that would be hard to solve by direct computation can be easily solved indirectly by genetic algorithms.

No simulation will ever be as good as the real world. But you can still learn a lot about the real world from a simulation.

Of course there are a zillion other hard problems that real creatures need to solve in the real world, but the Sims simulation effectively singled out a few of them and successfully showed they can be addressed quite effectively by genetic algorithms instead of a solution arrived at by direct design.

No simulation will ever be as good as the real world. But you can still learn a lot about the real world from a simulation.

You mean like the global warming models??

but the Sims simulation effectively singled out a few of them and successfully showed they can be addressed quite effectively by genetic algorithms instead of a solution arrived at by direct design.

What was demonstrated was virtual. Real joints, with real motive elements requiring real energy sources and real raw materials occuring in a hostile environment was not demonstrated.

Andrew,

All that was shown in the Sims simulation was the question of whether or not it is possible for any kind of sensors, motors, and joints to be represented in a binary string, whether a sequence of binary operations on that string can improve the operations of those sensors, motors, and joints in a given environment for a given goal, and whether that binary string is sufficiently small enough that it does not rule out the possibility for a real DNA to represent the same information encoded by the simulation.

Even though that question is far different from the one of: "did real creatures evolve" and even "can real creatures evolve", it is still a very important question, and the result was positive, meaning that the next set of questions can be asked in the context of the hypothesis proved by this first experiment.

That is how science marches forward. It will never prove real creatures did evolve, but successive successful experiments show that it is more likely that evolution alone cannot be precluded as a possible explanation for the genesis of life on earth.

All that was shown in the Sims simulation was the question of whether or not it is possible for any kind of sensors, motors, and joints to be represented in a binary string, whether a sequence of binary operations on that string can improve the operations of those sensors, motors, and joints in a given environment for a given goal, and whether that binary string is sufficiently small enough that it does not rule out the possibility for a real DNA to represent the same information encoded by the simulation.

That is all well and good, but we represent things all of the time when we draw it, write about it, speak about it or think about it. We can even imagine improving it. The fact is everything that man has built started out as a concept. Even a rock used as a weapon had to be conceived even if it was initially picked up to be used as food.

As for DNA coding, anything can be used to represent something else because representation is a concept(written while moving salt shaker to the left and stating this is New York). Eyeblinks were used to send a message by a POW. You want to code the human genome by drumbeats, it can be done, but you better have a lot of time to waste.

You want to use DNA to code a program? That is easy. With 4 bases ,

Adenilne (A)
Cytosine (C)
Guanine (G)
Thymine (T) or Uracil (U)

you have a four level code. You can match the bases to 2 bits in this manner for an example.

A = 00 (0)
C = 01 (1)
G = 10 (2)
T = 11 (3)

Pentium instruction INT 7 has a hex value of CD07 or in binary

1100110100000111

In DNA code mapped as above this would be

TATCAACT

As you can see each byte takes up 4 bases, so that a DNA the size of the Human genome could code a program of about 750 megabytes. In this day and age it may seem like a trivial amount of data since it will easily fit on a DVD. However, going through all of the progrms that could be written in that amount of memory would take a very long time. Here is a previous calculation giving an indication of the information that can be coded by the DNA.

The number of different items represented by that 6 billion bit sequence as I stated before is 2^6000000000. In base 10 that would be 10^1806179974. Now there are 60 seconds/minute * 60 minutes/hour * 24 hours/day * 366 days/year * 15,000,000,000 years = 4.74336E+17 (or 10^17.67608609) seconds since the purported start of the universe. We will assume this full time is available to generate the sequences represented by the 6 billion bit sequence. The huge number represented by 10^1806179974 breaks all my calculators so I must use logs. If we divide the number of items (10^1806179974) by the time available (10^17.67608609) we end up with 10^1806179956 combinations/second. Clearly that is a huge amount of "information".

Proofread better

Adenilne = ~~Adenoid~~ Adenine

Well let me set you straight. I made a comment. Someone did not like it. I like an argument so I kept it going.

The thing is this is non winnable to either side. Not that one side will not be proven correct. But that neither side will convince the other in my lifetime. Once my life is over, I guess I shall then see if faith was warrented.

If not then I have lost nothing. Science is cool. But what I see is that politics get involved way too much and in a lot of cases it appears, whether or not it does in actuality, to influence results. I like pure science that catters not to political pressures and PCism. If the science is real then it as also true.

Whether the science is geo-, bio-, or not there is a truth. The problem is we have to wait for one conclusion with political overtones. Before someone without politics gives the real answers.

There was a reporter after Columbine, who wanted to see how easy it was to get a gun at shows or on the WEB. He went into this with certain expectations. That is he thought it would be so easy to obtain a gun. He was proved wrong. The only person who would actually sell him a gun wanted him to drive 100+ miles to meet and make the sale in person, at a gun shop which could do a background check.

One example but there are hundreds like this based on science. Arts. Politics. Archeology. etc...

Politics is needed, and funny. It does however get in the way too often.

multi-spectral moron alert placemarker

Andrew,

The beauty of genetic algorithms is that the encoding format of a DNA string is independent from the process of evolution because the agent making changes to the string has no idea what it is doing.

As in real evolution, the Sims experiment used a random process to mate the genes of its virtual creatures, so it doesn't matter how the encoded creature are represented. That is, it is not necessary for anyone to "design" a particular representation that enables genetic algorithms to evolve a string that decodes to a creature that improves its results in the fitness test.

[Why don't you answer the question?]

You have not provided the evidence for the assertion that the evolved circuit performs better than the patented circuit even in its modified form. Not one measurement, not one graph, not one number.

Sigh -- just how many new pointless "objections" are you going to pull out of your hat in an endless parade of lame excuses to avoid having to actually deal with the questions that have been put to you, and the issues raised?

You're not fooling anyone with this game-playing. Except for perhaps yourself.

But just to give you one less cheap excuse to avoid the issue, here's more information on the evolved circuit: "Evolving Inventions", John R. Koza, Martin A. Keane and Matthew J. Streeter, "Scientific American", Feb. 2003. For a more technical treatment, see: Matthew J. Streeter, Martin A. Keane, John R. Koza: Routine Duplication of Post-2000 Patented Inventions by Means of Genetic Programming. EuroGP 2002: 26-36.

So what's your excuse going to be *now*, Mr. Evasive?

While you're at it, you might want to take a gander at the following and explain why evolution doesn't actually work in *these* research projects either, even though it certainly seems to perform just fine:

Justin Balthrop, Fernando Esponda, Stephanie Forrest, Matthew Glickman: Coverage and Generalization in an Artificial Immune System. 3-10
Mauro Birattari, Thomas Stützle, Luis Paquete, Klaus Varrentrapp: A Racing Algorithm for Configuring Metaheuristics. 11-18
T. M. Blackwell, Peter J. Bentley: Dynamic Search With Charged Swarms. 19-26
Christian Blum: Ant Colony Optimization For The Edge-weighted k-cardinality Tree Problem. 27-34
Christian Blum, Michael Sampels, Mark Zlochin: On A Particularity In Model-based Search. 35-42
Thang Nguyen Bui, Lisa C. Strite: An Ant System Algorithm For Graph Bisection. 43-51
John A. Bullinaria: The Evolution Of Variable Learning Rates. 52-59
Alex Conradie, Risto Miikkulainen, Christiaan Aldrich: Adaptive Control Utilising Neural Swarming. 60-67
R. J. W. Hodgson: Partical Swarm Optimization Applied To The Atomic Cluster Optimization Problem. 68-73
Christian Keber, Matthias G. Schuster: Option Valuation With Generalized Ant Programming. 74-81
Nicole P. Leahy: Effects Of Agent Representation On The Behavior Of A Non-reciprocal Cooperation Game. 82-87
Suihong Liang, A. Nur Zincir-Heywood, Malcolm I. Heywood: Intelligent Packets For Dynamic Network Routing Using Distributed Genetic Algorithm. 88-96
Keith E. Mathias, Jason S. Byassee: Agent Support Of Genetic Search In An Immunological Model Of Sparse Distributed Memory. 97-104
Daniel Merkle, Martin Middendorf: Studies On The Dynamics Of Ant Colony Optimization Algorithms. 105-112
Kenneth O. Stanley, Risto Miikkulainen: Continual Coevolution Through Complexification. 113-120
Keiki Takadama, Yutaka L. Suematsu, Norberto Eiji Nawa, Katsunori Shimohara: Cross-validation In Multiagent-based Simulation: Analyzing Evolutionary Bargaining Agents. 121-128
Shin Ando, Hitoshi Iba: Ant Algorithm For Construction Of Evolutionary Tree. 131
Josh C. Bongard, Rolf Pfeifer: Behavioural Selection Pressure Generates Hierarchical Genetic Regulatory Networks. 132
Mariusz Boryczka, Zbigniew J. Czech: Solving Approximation Problems By Ant Colony Programming. 133
Mathieu S. Capcarrère: Evolution Of Asynchronous Cellular Automata: Finding The Good Compromise. 134
Sanjoy Das, Shekhar V. Gosavi, William H. Hsu, Shilpa A. Vaze: An Ant Colony Approach For The Steiner Tree Problem. 135
Tom Lenaerts, A. Defaweux, Piet van Remortel, Bernard Manderick: An Individual-based Approach To Multi-level Selection. 136
Olivier Pelletier, André Weimerskirch: Algorithmic Self-assembly Of DNA Tiles And Its Application To Cryptanalysis. 139-146
Bernard Yurke, Friedrich C. Simmel: A DNA-based Three-state Device. 147-152
Julie Beaulieu, Christian Gagné, Marc Parizeau: Lens System Design And Re-engineering With Evolutionary Algorithms. 155-162
John C. Gallagher, Saranyan Vigraham: A Modified Compact Genetic Algorithm For The Intrinsic Evolution Of Continuous Time Recurrent Neural Networks. 163-170
Morten Hartmann, Frode Eskelund, Pauline C. Haddow, Julian F. Miller: Evolving Fault Tolerance On An Unreliable Technology Platform. 171-177
Uwe Tangen: An Evolvable Micro-controller Or What's New About Mutations? 178-186
Nattee Niparnan, Prabhas Chongstitvatana: An Improved Genetic Algorithm For The Inference Of Finite State Machine. 189
Brian K. Beachkofski, Gary B. Lamont: Evolutionary Programming Based Stratified Design Space Sampling. 193-200
Carlos A. Coello Coello, Ricardo Landa Becerra: Adding Knowledge And Efficient Data Structures To Evolutionary Programming: A Cultural Algorithm For Constrained Optimization. 201-209
Mikhail A. Semenov: Convergence Velocity Of Evolutionary Algorithm With Self-adaptation. 210-213
Man Leung Wong, Shing Yan Lee, Kwong-Sak Leung: A Hybrid Data Mining Approach To Discover Bayesian Networks Using Evolutionary Programming. 214-222
Yong Liu, Xin Yao: Search Step Size Control In Fast Evolutionary Programming. 225
John DeLaurentis, Lauren Ferguson, William E. Hart: On The Convergence Properties Of A Simple Self-adaptive Evolutionary Algorithm. 229-237
Thomas Jansen, Kenneth A. De Jong: An Analysis Of The Role Of Offspring Population Size In EAs. 238-246
Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff: On The Dynamics Of Evolutionary Multi-objective Optimization. 247-256
David J. John: Co-evolution With The Bierwirth-Mattfeld Hybrid Scheduler. 259
Uwe Aickelin, Larry Bull: Partnering Strategies For Fitness Evaluation In A Pyramidal Evolutionary Algrorithm. 263-270
Laura A. Albert, David E. Goldberg: Efficient Discretization Scheduling In Multiple Dimensions. 271-278
Matthew Alden, Aard-Jan van Kesteren, Risto Miikkulainen: Eugenic Evolution Utilizing A Domain Model. 279-286
Helio J. C. Barbosa, Afonso C. C. Lemonge: An Adaptive Penalty Scheme In Genetic Algorithms For Constrained Optimiazation Problems. 287-294
Jason S. Byassee, Keith E. Mathias: Expediting Genetic Search With Dynamic Memory. 295-302
Erick Cantú-Paz: Feature Subset Selection By Estimation Of Distribution Algorithms. 303-310
Erick Cantú-Paz: On Random Numbers And The Performance Of Genetic Algorithms. 311-318
Jian-Hung Chen, David E. Goldberg, Shinn-Ying Ho, Kumara Sastry: Fitness Inheritance In Multi-objective Optimization. 319-326
Sung-Soon Choi, Byung Ro Moon: Isomorphism, Normalization, And A Genetic Algorithm For Sorting Network Optimization. 327-334
Sung-Soon Choi, Byung Ro Moon: More Effective Genetic Search For The Sorting Network Problem. 335-342
Federico Divina, Elena Marchiori: Evolutionary Concept Learning. 343-350
César A. Galindo-Legaria, Florian Waas: The Effect Of Cost Distributions On Evolutionary Optimization Algorithms. 351-358
Simon M. Garrett, Joanne H. Walker: Combining Evolutionary And Non-evolutionary Methods In Tracking Dynamic Global Optima. 359-366
William A. Greene: A Genetic Algorithm With Self-distancing Bits But No Overt Linkage. 367-374
Steffen G. Hohmann, Johannes Schemmel, Felix Schürmann, Karlheinz Meier: Exploring The Parameter Space Of A Genetic Algorithm For Training An Analog Neural Network. 375-382
William H. Hsu, Haipeng Guo, Benjamin B. Perry, Julie A. Stilson: A Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data. 383-390
Michael Hüsken, Christian Igel: Balancing Learning And Evolution. 391-398
Hisao Ishibuchi, Takashi Yamamoto: Fuzzy Rule Selection By Data Mining Criteria And Genetic Algorithms. 399-406
Jung Hwan Kim, Byung Ro Moon: Neuron Reordering For Better Neuro-genetic Hybrids. 407-414
Steven O. Kimbrough, Ming Lu, David Harlan Wood, Dong-Jun Wu: Exploring A Two-market Genetic Algorithm. 415-422
Thomas E. Koch, Andreas Zell: MOCS: Multi-objective Clustering Selection Evolutionary Algorithm. 423-430
Sujay V. Kumar, S. Ranji Ranjithan: Evaluation Of The Constraint Method-based Evolutionary Algorithm (CMEA) For A Three-objective Problem. 431-438
Marco Laumanns, Lothar Thiele, Eckart Zitzler, Kalyanmoy Deb: Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization. 439-447
Yong Liu, Xin Yao: Maintaining Population Diversity By Minimizing Mutual Information. 448-455
Jiangming Mao, Kotaro Hirasawa, Jinglu Hu, Junichi Murata: Increasing Robustness Of Genetic Algorithm. 456-462
Anil Menon: The Point Of Point Crossover: Shuffling To Randomness. 463-471
Peter Merz: A Comparison Of Memetic Recombination Operators For The Traveling Salesman Problem. 472-479
Mark M. Meyesenburg, Daniel Hoelting, Duane McElvain, James A. Foster: How Random Generator Quality Impacts GA Performance. 480-487
Miguel Nicolau, Conor Ryan: LINKGAUGE: Tackling Hard Deceptive Problems With A New Linkage Learning Genetic Algorithm. 488-494
Gabriela Ochoa: Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic. 495-502
Liviu Panait, Sean Luke: A Comparison Of Two Competitive Fitness Functions. 503-511
Martin Pelikan, David E. Goldberg, Shigeyoshi Tsutsui: Combining The Strengths Of Bayesian Optimization Algorithm And Adaptive Evolution Strategies. 512-519
Robin C. Purshouse, Peter J. Fleming: Why Use Elitism And Sharing In A Multi-objective Genetic Algorithm? 520-527
Kumara Sastry, David E. Goldberg: Genetic Algorithms, Efficiency Enhancement, And Deciding Well With Differing Fitness Variances. 528-535
Kumara Sastry, David E. Goldberg: Genetic Algorithms, Efficiency Enhancement, And Deciding Well With Differing Fitness Bias Values. 536-543
Dong-il Seo, Byung Ro Moon: Voronoi Quantizied Crossover For Traveling Salesman Problem. 544-552
Hiroshi Someya, Masayuki Yamamura: Robust Evolutionary Algorithms With Toroidal Search Space Conversion For Function Optimization. 553-560
Alexander V. Spirov, Alexander B. Kazansky: Jumping Genes-mutators Can Rise Efficacy Of Evolutionary Search. 561-568
Kenneth O. Stanley, Risto Miikkulainen: Efficient Reinforcement Learning Through Evolving Neural Network Topologies. 569-577
Christopher R. Stephens, Riccardo Poli, Alden H. Wright, Jonathan E. Rowe: Exact Results From A Coarse Grained Formulation Of The Dynamics Of Variable-length Genetic Algorithms. 578-585
Christopher Stone, Jim Smith: Strategy Parameter Variety In Self-adaptation Of Mutation Rates. 586-593
Hal Stringer, Annie S. Wu: A Simple Method For Detecting Domino Convergence And Identifying Salient Genes Within A Genetic Algorithm. 594-601
Ashutosh Tiwari, Rajkumar Roy: Variable Dependence Interaction And Multi-objective Optimisation. 602-609
Huai-Kuang Tsai, Jinn-Moon Yang, Cheng-Yan Kao: Applying Genetic Algorithms To Finding The Optimal Gene Order In Displaying The Microarray Data. 610-617
Alexander F. Tulai, Franz Oppacher: Combining Competitive And Cooperative Coevolution For Training Cascade Neural Networks. 618-625
Clarissa Van Hoyweghen, David E. Goldberg, Bart Naudts: From Twomax To The Ising Model: Easy And Hard Symmetrical Problems. 626-633
Dana Vrajitoru: Simulating Gender Separation With Genetic Algorithms. 634-641
Alden H. Wright, Jonathan E. Rowe, Riccardo Poli, Christopher R. Stephens: A Fixed Point Analysis Of A Gene Pool GA With Mutation. 642-649
Shengxiang Yang: Adaptive Non-uniform Crossover Based On Statistics For Genetic Algorithms. 650-657
Zhong-Yao Zhu, Kwong-Sak Leung: An Enhanced Annealing Genetic Algorithm For Multi-objective Optimization Problems. 658-665
Eckart Zitzler, Marco Laumanns, Lothar Thiele, Carlos M. Fonseca, Viviane Grunert da Fonseca: Why Quality Assessment Of Multiobjective Optimizers Is Difficult. 666-674
Jaume Bacardit, Josep Maria Garrell i Guiu: Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System. 677
Dirk Devogelaere, Marcel Rijckaert: Influences Of Clustering Modifications On The Performatnce Of The Genetic Algorithm Driven Clustering Algorithm. 678
A. Sima Etaner-Uyar, A. Emre Harmanci: Preserving Diversity In Changing Environments Through Diploidy With Adaptive Dominance. 679
William H. Hsu, Cecil P. Schmidt, James A. Louis: Genetic Algorithm Wrappers For Feature Subset Selection In Supervised Inductive Learning. 680
Chien-Geng Huang: A Study Of Fitness Proportional Mate Selection Schemes In Genetic Algorithms. 681
Chien-Feng Huang: A Markov Chain Analysis Of Fitness Proportional Mate Selection Schemes In Genetic Algorithm. 682
Yaochu Jin, Bernhard Sendhoff: Incorporation Of Fuzzy Preferences Into Evolutionary Multiobjective Optimization. 683
Nazan Khan, David E. Goldberg, Martin Pelikan: Multiple-objective Bayesian Optimization Algorithm. 684
Jong-Pil Kim, Byung Ro Moon: A Hybrid Genetic Search For Circuit Bipartitioning. 685
Yong-Hyuk Kim, Byung Ro Moon: Visualization Of The Fitness Landscape, A Steady-state Genetic Search, And Schema Traces. 686
Kosmas Knödler, Jan Poland, Andreas Zell, Alexander Mitterer: Memetic Algorithms For Combinatorial Optimization Problems In The Calibration Of Modern Combustion Engines. 687
Alexander Kosorukoff: Using Incremental Evaluation And Adaptive Choice Of Operators In A Genetic Algorithm. 688
Seung-Kyu Lee, Dong-il Seo, Byung Ro Moon: A Hybrid Genetic Algorithm For Optimal Hexagonal Tortoise Problem. 689
Penousal Machado, Jorge Tavares, Francisco B. Pereira, Ernesto Costa: Vehicle Routing Problem: Doing It The Evolutionary Way. 690
Mark M. Meyesenburg, Dan Hoelting, Duane McElvain, James A. Foster: A Genetic Algorithm-specific Test Of Random Generator Quality. 691
James E. Pettinger, Richard M. Everson: Controlling Genetic Algorithms With Reinforcement Learning. 692
Oclair Prado, Fernando J. Von Zuben: An Integrated System For Phylogenetic Inference Using Evolutionary Algorithms. 693
Robert S. Roos, Tiffany Bennett, Jennifer Hannon, Elizabeth Zehner: A Genetic Algorithm For Improved Shellsort Sequences. 694
Franz Rothlauf: The Influence Of Binary Representations Of Integers On The Performance Of Selectorecombinative Genetic Algorithms. 695
Ivan Sekaj, Martin Foltin, Michal Gonos: Genetic Algorithm Based Adaptive Control Of An Electromechanical MIMO System. 696
Anabela Simões, Ernesto Costa: Parametric Study To Enhance The Genetic Algorithm's Performance When Using Transformation. 697
Anabela Simões, Ernesto Costa: Using GAs To Deal With Dynamic Environments: A Comparative Study Of Several Approaches Based On Promoting Diversity. 698
Abhishek Singh, David E. Goldberg, Ying-Ping Chen: Modified Linkage Learning Genetic Algorithm For Difficult Non-stationary Problems. 699
Tapio Tyni, Jari Ylinen: Bi-directional Circular Linked Lists In Fitness Caching. 700
Takanori Ueda, Nobuto Koga, Isao Ono, Masahiro Okamoto: Application Of Numerical Optimization Technique Based On Real-coded Genetic Algorithm To Inverse Problem In Biochemical Systems. 701
Shinya Watanabe, Tomoyuki Hiroyasu, Mitsunori Miki: LCGA: Local Cultivation Genetic Algorithm For Multi-objective Optimization Problems. 702
Annie S. Wu, Ivan Garibay: The Proportional Genetic Algorithm Representation. 703
Tina Yu, Julian F. Miller: Climbing Unimodal Landscapes With Neutrality: A Case Study Of The One-max Problem. 704
Atif Azad, Conor Ryan, Mark E. Burke, Ali R. Ansari: A Re-examination Of The Cart Centering Problem Using The Chorus System. 707-715
Raymond Burke, Steven Gustafson, Graham Kendall: A Survey And Analysis Of Diversity Measures In Genetic Programming. 716-723
Manuel Clergue, Philippe Collard, Marco Tomassini, Leonardo Vanneschi: Fitness Distance Correlation And Problem Difficulty For Genetic Programming. 724-732
Raphael Crawford-Marks, Lee Spector: Size Control Via Size Fair Genetic Operators In The PushGP Genetic Programming System. 733-739
R. Groß, K. Albrecht, Wolfgang Kantschik, Wolfgang Banzhaf: Evolving Chess Playing Programs. 740-747
Lutz H. Hamel: Breeding Algebraic Structures - An Evolutionary Approach To Inductive Equational Logic Programming. 748-755
Daniel Howard, Simon C. Roberts, Conor Ryan: Machine Vision: Exploring Context With Genetic Programming. 756-763
William H. Hsu, Steven M. Gustafson: Genetic Programming And Multi-agent Layered Learning By Reinforcements. 764-771
Jianjun Hu, Erik D. Goodman, Kisung Seo, Min Pei: Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms. 772-779
Jianjun Hu, Kisung Seo, Shaobo Li, Zhun Fan, Ronald C. Rosenberg, Erik D. Goodman: Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming. 780-787
Hitoshi Iba, Erina Sakamoto: Inference Of Differential Equation Moels By Genetic Programming. 788-795
Kosuke Imamura, Robert B. Heckendorn, Terence Soule, James A. Foster: Abstention Reduces Errors - decision Abstaining N-version Genetic Programming. 796-803
Jeremy Kubica, Eleanor G. Rieffel: Collaborating With A Genetic Programming System To Generate Modular Robotic Code. 804-811
William B. Langdon: Convergence Rates For The Distribution Of Program Outputs. 812-819
Sean Luke, Liviu Panait: Is The Perfect The Enemy Of The Good? 820-828
Sean Luke, Liviu Panait: Lexicographic Parsimony Pressure. 829-836
Peter Martin: An Analysis Of Random Number Generators For A Hardware Implementation Of Genetic Programming Using FPGAs And Handel-C. 837-844
Peter Martin, Riccardo Poli: Crossover Operators For A Hardware Implementation Of GP Using FPGAs And Handel-C. 845-852
Nicholas Freitag McPhee, Riccardo Poli: Using Schema Theory To Explore Interactions Of Multiple Operators. 853-860
Johan Parent, Ann Nowé: Evolving Compression Preprocessors With Genetic Programming. 861-867
Riccardo Poli, Christopher R. Stephens, Alden H. Wright, Jonathan E. Rowe: On The Search Biases Of Homologuous Crossover In Linear Genetic Programming And Variable-length Genetic Algorithms. 868-876
Matthew J. Streeter, Martin A. Keane, John R. Koza: Iterative Refinement Of Computational Circuits Using Genetic Programming. 877-884
Jason Cooper, Chris Hinde: Comparison Of Evolving Against Peers And Fixed Opponents Using Corewars. 887
Christian Gagné, Marc Parizeau: Open BEAGLE: A New C++ Evolutionary Computation Framework. 888
Mario Giacobini, Marco Tomassini, Leonardo Vanneschi: How Statistics Can Help In Limiting The Number Of Fitness Cases In Genetic Programming. 889
Hironobu Katagiri, Kotaro Hirasawa, Jinglu Hu, Junichi Murata: A New Model To Realize Variable Size Genetic Network Programming. 890
Emin Erkan Korkmaz, Göktürk Ücoluk: Controlling The Genetic Programming Search. 891
Carlos Oliver-Morales, Katya Rodríguez Vázquez: MB GP In Modelling And Prediction. 892
Ronald Olson, Brock Wilcox: Self-improvement For The ADATE Automatic Programming System. 893
Mark S. Withall, Chris J. Hinde, Roger G. Stone: Evolving Readable Perl. 894
Larry Bull: Lookahead And Latent Learning In ZCS. 897-904
Larry Bull, Toby O'Hara: Accuracy-based Neuro And Neuro-fuzzy Classifier Systems. 905-911
Martin Danek, Robert E. Smith: XCS Applied To Mapping FPGA Architectures. 912-919
Chunsheng Fu, Lawrence Davis: A Modified Classifier System Compaction Algorithm. 920-925
Samuel Landau, Sébastien Picault, Oliver Sigaud, Pierre Gérard: A Comparison Between ATNoSFERES And XCSM. 926-933
Xavier Llorà, Josep Maria Garrell i Guiu: Coevolving Different Knowledge Representations With Fine-grained Parallel Learning Classifier Systems. 934-941
Peter Ross, Sonia Schulenburg, Javier G. Marín-Blázquez, Emma Hart: Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems. 942-948
Larry Bull, Dave Wyatt, Ian C. Parmee: Towards The Use Of XCS In Interactive Evolutionary Design. 951
Gabriella Kókai, Zoltán Tóth, Szilvia Zvada: An Experimental Comparison Of Genetic And Classical Concept Learning Methods. 952
Filippo Neri: Cooperative Concept Learning By Means Of A Distributed GA. 953-956
Marc Ebner, Hans-Georg Breunig, Jürgen Albert: On The Use Of Negative Selection In An Artificial Immune System. 957-964
Alexander Kosorukoff, David E. Goldberg: Evolutionary Computation As A Form Of Organization. 965-972
Hassan Z. Masum, Steffen Christensen, Franz Oppacher: The Turing Ratio: Metrics For Open-ended Tasks. 973-980
Xiaotong Wang, Lawrence Davis, Chunsheng Fu: Genetic Algorithms And Fine-grained Topologies For Optimization. 981-988
Maribel García Arenas, Brad Dolin, Juan J. Merelo Guervós, Pedro A. Castillo Valdivieso, Ignacio Fernández De Viana, Marc Schoenauer: JEO: Java Evolving Objects. 991-994
L. Barone, L. While, P. Hingston: Designing Crushers With A Multi-objective Evolutionary Algorithm. 995-1002
Bir Bhanu, Yingqiang Lin: Learning Composite Operators For Object Detection. 1003-1010
Anthony Brabazon, Michael O'Neill, Robin Matthews, Conor Ryan: Grammatical Evolution And Corporate Failure Prediction. 1011-1018
Erick Cantú-Paz, Chandrika Kamath: Evolving Neural Networks For The Classification Of Galaxies. 1019-1026
Robert Carr, William Hart, Natalio Krasnogor, Jonathan Hirst, Edmund K. Burke, James Smith: Alignment Of Protein Structures With A Memetic Evolutionary Algorithm. 1027-1034
Deborah R. Carvalho, Alex Alves Freitas: A Genetic Algorithm With Sequential Niching For Discovering Small-disjunct Rules. 1035-1042
Flor A. Castillo, Ken A. Marshall, James L. Green, Arthur K. Kordon: Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations. 1043-1047
Ping Chen, Zhaohui Fu, Ping Chen, Andrew Lim: Using Genetic Algorithms To Solve The Yard Allocation Problem. 1049-1056
Clare Bates Congdon: Gaphyl: An Evolutionary Algorithms Approach For The Study Of Natural Evolution. 1057-1064
Anthony Di Pietro, Lyndon While, Luigi Barone: Learning In RoboCup Keepaway Using Evolutionary Algorithms. 1065-1072
Zhun Fan, Kisung Seo, Ronald C. Rosenberg, Jianjun Hu, Erik D. Goodman: Exploring Multiple Design Topologies Using Genetic Programming And Bond Graphs. 1073-1080
Fabio A. Gonzalez, Dipankar Dasgupta: An Imunogenetic Technique To Detect Anomalies In Network Traffic. 1081-1088
Karim Hamza, Haitham Mahmoud, Kazuhiro Saitou: Design Optimization Of N-shaped Roof Trusses. 1089-1096
Daniel Howard, Simon C. Roberts: Application Of Genetic Programming To Motorway Traffic Modelling. 1097-1104
Yaochu Jin, Bernhard Sendhoff: Fitness Approximation In Evolutionary Computation - a Survey. 1105-1112
Li-Shan Kang, Zhou Kang, Yan Li, Hugo de Garis: A Two Level Evolutionary Modeling System For Financial Data. 1113-1118
Yung-Keun Kwon, Sung-Deok Hong, Byung Ro Moon: A Genetic Hybrid For Critical Heat Flux Function Approximation. 1119-1125
Sang-Yon Lee, Sung-Soon Choi, Byung Ro Moon: Search Improvement By Genetic Algorithms With A Semiotic Network. 1126-1132
Derek S. Linden: Antenna Design Using Genetic Algorithm. 1133-1140
David Montana, Talib Hussain, Tushar Saxena: Adaptive Reconfiguration Of Data Networks Using Genetic Algorithms. 1141-1149
Jason H. Moore, Lance W. Hahn, Marylyn D. Ritchie, Tricia A. Thornton, Bill C. White: Application Of Genetic Algorithms To The Discovery Of Complex Models For Simulation Studies In Human Genetics. 1150-1155
Boris Naujoks, Werner Haase, Jörg Ziegenhirt, Thomas Bäck: Multi Objective Airfoil Design Using Single Parent Populations. 1156-1163
V. Oduguwa, R. Roy: Multi-objective Optimisation Of Rolling Rod Product Design Using Meta-modelling Approach. 1164-1171
Eun-Jong Park, Yong-Hyuk Kim, Byung Ro Moon: Genetic Search For Fixed Channel Assignment Problem With Limited Bandwidth. 1172-1179
Khaled Rasheed, Swaroop Vattam, Xiao Ni: Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization. 1180-1187
Wesley Romão, Alex Alves Freitas, Roberto C. S. Pacheco: A Genetic Algorithm For Discovering Interesting Fuzzy Prediction Rules: Applications To Science And Technology Data. 1188-1195
Brian J. Ross, Anthony G. Gualtieri, Frank Fueten, Paul Budkewitsch: Hyperspectral Image Analysis Using Genetic Programming. 1196-1203
Yuji Sato: Voice Conversion Using Interactive Evolution Of Prosodic Control. 1204-1211
J. David Schaffer, Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin: Improving Digital Video Commercial Detectors With Genetic Algorithms. 1212-1218
Ivan Tanev, Takashi Uozumi, Yoshiharu Morotome: An Application Service Provider Approach For Hybrid Evolutionary Algorithm-based Real-world Flexible Job Shop Scheduling Problem. 1219-1226
Mark S. Voss, Xin Feng: A New Methodology For Emergent System Identification Using Particle Swarm Optimization (PSO) And The Group Mehtod Data Handling (GMDH). 1227-1232
Joachim Wegener, Kerstin Buhr, Hartmut Pohlheim: Automatic Test Data Generation For Structural Testing Of Embedded Software Systems By Evolutionary Testing. 1233-1240
Abdulnasser Younes, Hamada Ghenniwa, Shawki Areibi: An Adaptive Genetic Algorithm For Multi Objective Flexible Manufacturing Systems. 1241-1248
Jian Zhang, Xiaohui Yuan, Bill P. Buckles: A Fast Evolution Strategies Based Approach To Image Registration. 1249-1256
Sung-Soon Choi, Byung Ro Moon: Optimized Interest Metric Of Rules And One-to-one Marketing Using Connection Networks. 1259
H. W. Chong, Sam Kwong: A Genetic Algorithm For Joint Optimization Of Spare Capacity And Delay In Self-healing Network. 1260
L. Elliott, David B. Ingham, A. G. Kyne, N. S. Mera, M. Pourkashanian, C. W. Wilson: A Real Coded Genetic Algorithm For The Optimisation Of Reaction Rate Parameters For Chemical Kinetic Modelling In A Perfectly Stirred Reactor. 1261
Sean L. Forman: Congressional Redistricting Using A TSP-based Genetic Algorithm. 1262
Héctor Fernando Gómez García, Arturo González Vega, Arturo Hernández Aguirre, Carlos A. Coello Coello: Efficient Affine 2D-image Registration Using Evolutionary Strategies. 1263
Marco César Goldbarg, Elizabeth Ferreira Gouvea, Francisco Dantas de Medeiros Neto: Piston Pump Mobile Unity Tour Problem: An Evolutionary View. 1264
Alberto Gomez, David de la Fuente, Jose Parreño, Javier Puente: Using Genetic Algorithms To Optimize Guillotine Cutting Operations. 1265
Alex C. H. Ho, Sam Kwong: Optimization Of CDMA Based Wireless System. 1266
Zhou Ji, Dipankar Dasgupta: Modeling Convection Coefficients With Genetic Algorithms. 1267
Otavio Larsen, Alex Alves Freitas, Júlio C. Nievola: Constructing X-of-n Attributes With A Genetic Algorithm. 1268
Shouichi Matsui, Isamu Watanabe, Ken-ichi Tokoro: An Efficient Genetic Algorithm For Fixed Channel Assignment Problem With Limited Bandwidth Constraint. 1269
Fernando E. B. Otero, Monique M. S. Silvia, Alex Alves Freitas: Genetic Programming For Attribute Construction In Data Mining. 1270
Supiya Ujjin, Peter J. Bentley: Evolving Good Recommendations. 1271
Edgar E. Vallejo, Fernando Ramos: Evolving Finite Automata With Two-dimensional Output For DNA Recognition And Visualization. 1272
Andre Vogel, Marco Fischer, Tobias Teich: Real-world Shop Floor Scheduling By Ant Colony Optimization. 1273
L. Wu, Walter D. Potter, K. Rasheed, H. Thistle, J. Ghent, D. Twardus, M. Teske: A Comparison of Genetic Algorithm Methods In Aerial Spray Deposition Management. 1274
Xiaoming Yu, Alessandro Fin, Franco Fummi, Elizabeth M. Rudnick: Functional Test Generation For Digital Integrated Circuits Using A Genetic Algorithm. 1275
Jesse B. Zydallis, Todd A. Sriver, Gary B. Lamont: Multiobjective Evolutionary Algorithm Approach For Solving Integer Based Optimization Problems. 1276
Peter Augustsson, Krister Wolff, Peter Nordin: Creation Of A Learning, Flying Robot By Means Of Evolution. 1279-1285
Gary B. Parker: Learning Area Coverage Using The Co-evolution Of Model Parameters. 1286-1294
Karl Hedman, David Persson, Per Skoglund, Dan Wiklund, Krister Wolff, Peter Nordin: Sensing And Direction In Locomotion Learning With A Random Morphology Robot. 1297
Jumpol Polvichai, Pradeep K. Khosla: Applying Dynamic Networks To Improve Learning Performances Of An Evolutionary Behavior Programming System For Mobile Robots In Dynamic Environments. 1298
Hisao Ishibuchi, Tadashi Yoshida, Tadahiko Murata: Balance Between Genetic Search And Local Search In Hybrid Evolutionary Multi-criterion Optimization Algorithms. 1301-1308
Soonchul Jung, Byung Ro Moon: A Hybrid Genetic Algorithm For The Vehicle Routing Problem With Time Windows. 1309-1316
Marc Reimann, Michael Stummer, Karl Doerner: A Savings Based Ant System For The Vehicle Routing Problem. 1317-1326
André Baresel, Harmen Sthamer, Michael Schmidt: Fitness Function Design To Improve Evolutionary Structural Testing. 1329-1336
Leonardo Bottaci: Instrumenting Programs With Flag Variables For Test Data Search By Genetic Algorithms. 1337-1342
Maria Cláudia Figueiredo Pereira Emer, Silva Regina Vergilio: GPTesT: A Testing Tool Based On Genetic Programming. 1343-1350
Mark Harman, Robert M. Hierons, Mark Proctor: A New Representation And Crossover Operator For Search-based Optimization Of Software Modularization. 1351-1358
Mark Harman, Lin Hu, Robert M. Hierons, André Baresel, Harmen Sthamer: Improving Evolutionary Testing By Flag Removal. 1359-1366
Colin Kirsopp, Martin J. Shepperd, John Hart: Search Heuristics, Case-based Reasoning And Software Project Effort Prediction. 1367-1374
Brian S. Mitchell, Spiros Mancoridis: Using Heuristic Search Techniques To Extract Design Abstractions From Source Code. 1375-1382
Terry Van Belle, David H. Ackley: Code Factoring And The Evolution Of Evolvability. 1383-1390
Hans-Gerhard Groß, Nikolas Mayer: Evolutionary Testing In Component-based Real-time System Construction. 1393

The above can be found in the publication: William B. Langdon, Erick Cantú-Paz, Keith E. Mathias, Rajkumar Roy, David Davis, Riccardo Poli, Karthik Balakrishnan, Vasant Honavar, Günter Rudolph, Joachim Wegener, Larry Bull, Mitchell A. Potter, Alan C. Schultz, J. F. Miller, E. Burke, Natasa Jonoska (Eds.): GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002. Morgan Kaufmann 2002, ISBN 1-55860-878-8

Give it up, Andrew, you're just looking silly. There's a whole world of significant evolutionary research results out there that you can't just make go away by wishing hard enough, or being evasive enough.