Genetic Programming

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Genetic Programming (GP), an evolutionary based methodology inspired by biological evolution to optimize computer programs, in particular game playing programs. It is a machine learning technique used to optimize a population of programs, for instance to maximize the winning rate versus a set of opponents, after modifying evaluation weights or search parameter. =Evolutionary Programming= Evolutionary programming is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve. The term was coined by Lawrence J. Fogel in 1960.

=Supersets= Genetic Programming is subset of a chain of subsequent fields in Artificial Intelligence.

Genetic Algorithms
Genetic Programming is a specialization of genetic algorithms (GA) where individuals are computer programs. This heuristic is routinely used to generate useful solutions to optimization and search problems. A genetic algorithm requires:
 * 1) Genetic representation
 * 2) Fitness function

performing the Genetic operations of
 * 1) Selection (genetic algorithm)
 * 2) Fitness proportionate selection
 * 3) Reward-based selection
 * 4) Stochastic universal sampling
 * 5) Tournament selection
 * 6) Truncation selection
 * 7) Crossover (genetic algorithm)

PBIL
Population-based incremental learning (PBIL) is a type of of genetic algorithm where the genotype of an entire population (probability vector) is evolved rather than individual members. The algorithm was proposed by Shumeet Baluja in 1994. The algorithm is simpler than a standard genetic algorithm, and in many cases leads to better results than a standard genetic algorithm.

Evolutionary Algorithms
Genetic algorithms belong to the larger class of evolutionary algorithms (EA). An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. EAs are individual components that participate in an artificial evolution (AE).

Evolutionary Computation
An evolutionary algorithm (EA) is subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. Evolutionary computation, introduced by John Henry Holland in the 1970s and more popular since 1990s mimics the population-based sexual evolution through reproduction of generations.

Computational Intelligence
Computational Intelligence (CI) is a set of Nature-inspired computational methodologies and approaches and field of Artificial Intelligence. It primarily includes many-valued logic or Fuzzy logic, Neural Networks, Evolutionary Computation, swarm intelligence and Artificial immune system.

=See also=
 * Artificial Intelligence
 * Automated Tuning
 * Differential Evolution in BBChess
 * Deep Learning
 * Dynamic Programming
 * GA in Falcon
 * Simulated Annealing
 * Trial and Error

=Publications=

1950 ...

 * Nils Barricelli (1954). Esempi numerici di processi di evoluzione, Methodos, pp. 45-68, 1954
 * Nils Barricelli (1957). Symbiogenetic evolution processes realized by artificial methods. Methodos: 143–182.

1960 ...

 * Nils Barricelli (1961). Numerical testing of evolution theories. Part I Theoretical introduction and basic tests. Department of Biology, Division of Molecular Biology, Vanderbilt University, Nashville, Tennessee, Acta Biotheoretica, Springer Netherlands, ISSN: 0001-5342
 * Woodrow W. Bledsoe (1962). An Analysis of Genetic Populations. Technical Report, Panoramic Research Inc., Palo Alto, California.
 * Woodrow W. Bledsoe (1962). The Evolutionary Method in Hill Climbing: Convergence Rates. Technical Report, Panoramic Research, Inc., Palo Alto, California. » Hill Climbing
 * Nils Barricelli (1963). Numerical testing of evolution theories. Part II preliminary tests of performance. symbiogenesis and terrestrial life. Department of Biology, Division of Molecular Biology, Vanderbilt University, Nashville, Tennessee, Acta Biotheoretica, Springer Netherlands, ISSN: 0001-5342

1970 ...

 * John Henry Holland (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. amazon.com

1980 ...

 * John Maynard Smith (1982). Evolution and the Theory of Games. Cambridge; New York, Cambridge University Press.
 * Robert Axelrod (1987). The Evolution of Strategies in the Iterated Prisoner’s Dilemma. in Lawrence D. Davis (ed.) Genetic Algorithms and Simulated Annealing. 2001 reprint as pdf
 * David E. Goldberg, John H. Holland (1988). Genetic Algorithms and Machine Learning. Machine Learning, Vol. 3
 * David E. Goldberg (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley
 * John Maynard Smith (1989). Evolutionary Genetics. Oxford; New York, Oxford University Press.
 * Greg M. Gupton (1989). Genetic Learning Algorithm Applied to the Game of Othello. Heuristic Programming in AI 1

1990 ...

 * John Koza (1990). Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Stanford University Computer Science Department technical report STAN-CS-90-1314, pdf
 * David E. Goldberg (1991). Real-coded Genetic Algorithms, Virtual Alphabets, and Blocking. Complex Systems, Vol. 5, No. 2
 * William Tunstall-Pedoe (1991). Genetic Algorithms Optimizing Evaluation Functions. ICCA Journal, Vol. 14, No. 3
 * John Koza (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, ISBN 0-262-11170-5
 * Byoung-Tak Zhang, Heinz Mühlenbein (1993). Evolving Optimal Neural Networks Using Genetic Algorithms with Occam's Razor. Complex Systems, Vol. 7, pdf
 * Shumeet Baluja (1994). Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Technical Report Carnegie Mellon University (CMU–CS–94–163)
 * John Koza (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, ISBN 0-262-11189-6
 * Deniz Yuret (1994). From Genetic Algorithms To Efficient Optimization. Ms.C. Thesis, Supervisor: Patrick Henry Winston
 * Kurt Thearling, Thomas S. Ray (1994). Evolving Multi-cellular Artificial Life. Artificial Life IV, R. Brooks and P. Maes (eds.), MIT Press
 * David E. Moriarty, Risto Miikkulainen (1994). Evolving Neural Networks to focus Minimax Search. AAAI-94, pdf

1995 ...

 * Omar Syed (1995). Applying Genetic Algorithms to Recurrent Neural Networks for Learning Network Parameters and Architecture, Masters Thesis, Case Western Reserve University
 * Eric B. Baum, Dan Boneh, Charles Garrett (1995). On Genetic Algorithms. COLT 1995
 * Christopher D. Rosin, Richard K. Belew (1995). Methods for Competitive Co-Evolution: Finding Opponents Worth Beating. ICGA 1995, CiteSeerX
 * Jean-Marc Alliot, Nicolas Durand (1995). A Genetic Algorithm to Improve an Othello Program. Artificial Evolution, LNCS 1063, Springer
 * Pieter Spronck (1996). Elegance: Genetic Algorithms in Neural Reinforcement Control. Master thesis, Delft University of Technology, pdf
 * Christopher D. Rosin, Richard K. Belew (1996). A Competitive Approach in Game Learning. COLT 1996, pdf
 * Bjørnar Tessem (1997). Genetic Algorithms for Analogical Mapping. In David W. Aha and Dietrich Wettschereck (eds.) Beyond Classification of Feature Vectors. ECML-97, CiteSeerX
 * Kurt Thearling, Thomas S. Ray (1997). Evolving Parallel Computation. Complex Systems, Vol. 10, No. 3
 * Volker Schnecke, Oliver Vornberger (1997). Hybrid genetic algorithms for constrained placement problems. IEEE Transactions on Evolutionary Computation, pdf
 * Christopher D. Rosin (1997). Coevolutionary Search Among Adversaries. Ph.D. thesis, University of California, San Diego, CiteSeerX
 * John Koza et al. (Eds.) (1998). Genetic Programming. Morgan Kaufmann Publishers, ISBN 1-55860-548-7
 * Norman Richards, David E. Moriarty, Risto Miikkulainen (1998). Evolving Neural Networks to Play Go. Applied Intelligence, Vol. 8, No. 1, pdf preprint
 * John Koza et al. (1999). Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann, ISBN 1-55860-543-6
 * Kumar Chellapilla, David B. Fogel (1999). Evolution, Neural Networks, Games, and Intelligence. Proceedings of the IEEE
 * Tim Taylor (1999). From Artificial Evolution to Artificial Life. Ph.D. Thesis, University of Edinburgh
 * Philip G. K. Reiser, Patricia J. Riddle (1999). Evolving Logic Programs to Classify Chess-Endgame Positions. Simulated Evolution and Learning, Canberra, Australia. Lecture Notes in Artificial Intelligence, No. 1585, Springer, pdf » Learning, Endgame

2000 ....
2001 2002 2003 2004
 * Ryszard Michalski (2000). LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning. Machine Learning 38
 * Thomas Philip Runarsson, Xin Yao (2000). Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, Vol. 4, No. 3
 * Eric B. Baum, Dan Boneh, Charles Garrett (2001). Where Genetic Algorithms Excel. Evolutionary Computation, Vol. 9, No. 1
 * Lothar M. Schmitt (2001). Theory of Genetic Algorithms. Theoretical Computer Science, Vol. 259, Nos. 1-2
 * Krzysztof Krawiec (2001). Genetic Programming with Local Improvement for Visual Learning from Examples. CAIP 2001
 * Yngvi Björnsson, Tony Marsland (2002). Learning Control of Search Extensions. Proceedings of the 6th Joint Conference on Information Sciences (JCIS 2002), pp. 446-449. pdf
 * David E. Goldberg (2002). The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers
 * Roderich Groß, Keno Albrecht, Wolfgang Kantschik, Wolfgang Banzhaf (2002). Evolving Chess Playing Programs. GECCO 2002, pdf
 * Krzysztof Krawiec (2002). Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks. Genetic Programming and Evolvable Machines, Vol. 3, No. 4
 * Matthew Pratola, Thomas Wolf (2003). Optimizing GOTOOLS' Search Heuristics using Genetic Algorithms. ICGA Journal, Vol. 26, No. 1 » Go
 * John Koza et al. (2003). Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Springer, ISBN 1-4020-7446-8
 * David B. Fogel, Timothy J. Hays (2003). New Results on Evolving Strategies in Chess. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
 * David Gleich (2003). Machine Learning in Computer Chess: Genetic Programming and KRK. Harvey Mudd College, pdf
 * Wee-Chong Oon, Yew Jin Lim (2003). An Investigation on Piece Differential Information in Co-Evolution on Games Using Kalah. CEC2003, Vol. 3, pdf
 * Lothar M. Schmitt (2003). Theory of Coevolutionary Genetic Algorithms. ISPA 2003
 * Krzysztof Krawiec, Bir Bhanu (2003). Coevolution and Linear Genetic Programming for Visual Learning. GECCO 2003
 * Adam Marczyk (2004) Genetic Algorithms and Evolutionary Computation. TalkOrigins Archive
 * Petr Aksenov (2004). Genetic algorithms for optimising chess position scoring. Masters thesis, pdf
 * David B. Fogel, Timothy J. Hays, Sarah L. Hahn, James Quon (2004). A Self-Learning Evolutionary Chess Program. Proceedings of the IEEE, Vol. 92 No. 12, pp. 1947-1954, CiteSeerX
 * Mathieu Autonès, Aryel Beck, Phillippe Camacho, Nicolas Lassabe, Hervé Luga, François Scharffe (2004). Evaluation of Chess Position by Modular Neural network Generated by Genetic Algorithm. EuroGP 2004
 * Shan-Tai Chen, Shun-Shii Lin, Li-Te Huang, Chun-Jen Wei (2004) Towards the Exact Minimization of BDDs-An Elitism-Based Distributed Evolutionary Algorithm. Journal of Heuristics, Vol. 10, No. 3

2005 ...
2006 2007 2008 2009
 * Ami Hauptman, Moshe Sipper (2005). Analyzing the Intelligence of a Genetically Programmed Chess Player. GECCO 2005
 * Ami Hauptman, Moshe Sipper (2005). GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players. EuroGP 2005, pdf
 * David B. Fogel, Timothy J. Hays, Sarah L. Hahn, James Quon (2005). Further Evolution of a Self-Learning Chess Program. IEEE Symposium on Computational Intelligence & Games, CiteSeerX
 * Kumara Sastry, David E. Goldberg, Graham Kendall (2005). Genetic Algorithms. Search Methodologies, Springer
 * Kokolo Ikeda (2005). Exemplar-based direct policy search with evolutionary optimization. CEC 2005
 * Thomas Philip Runarsson, Xin Yao (2005). Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 35, No. 2, pdf
 * Aguston E. Eiben, Marc Schoenauer (2005). Evolutionary Computing. arXiv:cs/0511004
 * Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer (2006). Universal Consistency and Bloat in GP. pdf
 * Nicolas Lassabe, Stéphane Sanchez, Hervé Luga, Yves Duthen (2006). Genetically Programmed Strategies For Chess Endgame. GECCO 2006, pdf
 * Borko Bošković, Sašo Greiner, Janez Brest, Viljem Žumer (2006). A Differential Evolution for the Tuning of a Chess Evaluation Function. IEEE Congress on Evolutionary Computation, 2006
 * Wolfgang Kantschik (2006). Genetische Programmierung und Schach. Ph.D. thesis, University of Dortmund, pdf (German)
 * Simon Lucas, Thomas Philip Runarsson (2006). Temporal Difference Learning versus Co-Evolution for Acquiring Othello Position Evaluation. IEEE CIG 2006
 * Ami Hauptman, Moshe Sipper (2007). Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess. EuroGP 2007, pdf » Mate Search
 * Moshe Sipper, Yaniv Azaria, Ami Hauptman, Yehonatan Shichel (2007). Designing an Evolutionary Strategizing Machine for Game Playing and Beyond. IEEE Transactions on Systems, Man, and Cybernetics, Part C, pdf
 * Ami Hauptman (2007). Evolving Machine Chess Players. EvoPhD 2007
 * Krzysztof Krawiec (2007). Generative Learning of Visual Concepts using Multiobjective Genetic Programming. Pattern Recognition Letters, Vol. 28, No. 16
 * Wojciech Jaśkowski, Krzysztof Krawiec, Bartosz Wieloch (2008). Evolving Strategy for a Probabilistic Game of Imperfect Information using Genetic Programming. Genetic Programming and Evolvable Machines, Vol. 9, No. 4, pdf
 * Borko Bošković, Sašo Greiner, Janez Brest, Aleš Zamuda, Viljem Žumer (2008). An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program. Advances in Differential Evolution, Studies in Computational Intelligence, ISBN: 978-3-540-68827-3
 * Omid David, Moshe Koppel, Nathan S. Netanyahu (2008). Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization. GECCO '08, arXiv:1711.06839
 * Pieter Spronck, Ida Sprinkhuizen-Kuyper, Eric Postma (2008). Deca: the doping-Driven Evolutionary Control Algorithm. Applied Artificial Intelligence, Vol. 22
 * Omid David, Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu (2009). Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions. GECCO '09, arXiv:1711.06840
 * Omid David (2009). Genetic Algorithms Based Learning for Evolving Intelligent Organisms. Ph.D. Thesis
 * Ami Hauptman (2009). Evolving Search Heuristics for Combinatorial Games with Genetic Programming. Ben-Gurion University of the Negev
 * Nur Merve Amil, Nicolas Bredèche, Christian Gagné, Sylvain Gelly, Marc Schoenauer, Olivier Teytaud (2009). A Statistical Learning Perspective of Genetic Programming. EuroGP 2009, pdf
 * Ian Stewart, Wenying Feng, Selim Akl (2009). A Further Improvement on a Genetic Algorithm. ITNG 2009
 * Gang Chen, Chor Ping Low, Zhonghua Yang (2009). Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms. IEEE Transactions Evolutionary Computation, Vol. 13, No. 3, pp. 661-673
 * Yun Bao, Erbo Zhao, Xiaocong Gan, Dan Luo, Zhangang Han (2009). A Review on Cutting-Edge Techniques in Evolutionary Algorithms. 5. ICNC 2009
 * Marcin Szubert (2009). Coevolutionary Reinforcement Learning and its Application to Othello. M.Sc. thesis, Poznań University of Technology, supervisor Krzysztof Krawiec, pdf

2010 ...
2011 2012 2013 2014
 * Dmitry Batenkov (2010). Hands-on introduction to genetic programming. ACM Crossroads, Vol. 17, No. 1
 * Omid David, Nathan S. Netanyahu, Yoav Rosenberg, Moshe Shimoni (2010). Genetic Algorithms for Automatic Classification of Moving Objects. ACM Genetic and Evolutionary Computation Conference (GECCO '10), Portland, OR
 * Omid David, Moshe Koppel, Nathan S. Netanyahu (2010). Genetic Algorithms for Automatic Search Tuning. ICGA Journal, Vol. 33, No. 2
 * Omid David, Moshe Koppel, Nathan S. Netanyahu (2010). Optimizing Selective Search in Chess. ICML Workshop on Machine Learning and Games, arXiv:1009.0550
 * Jean-Baptiste Hoock, Olivier Teytaud (2010). Bandit-Based Genetic Programming. 13th European Conference on Genetic Programming (2010), pdf
 * Borko Bošković (2010). Differential evolution for the Tuning of a Chess Evaluation Function. Ph.D. thesis, University of Maribor
 * James Glenn (2010). Optimizing genetic algorithm parameters for a stochastic game. ICEC 2010, pp. 199-206. SciTePress, ISBN 978-989-8425-31-7
 * Tomohiko Mitsuta, Lothar M. Schmitt (2010). Optimizing the Performance of GNU-chess with a Genetic Algorithm. HC 2010, pdf » GNU Chess
 * Kokolo Ikeda, Shigenobu Kobayashi, Hajime Kita (2010). Exemplar-Based Policy with Selectable Strategies and its Optimization Using GA. Transactions of the Japanese Society for Artificial Intelligence, Vol. 25, No. 2
 * Krzysztof Krawiec, Marcin Szubert (2010). Coevolutionary Temporal Difference Learning for small-board Go. IEEE Congress on Evolutionary Computation
 * Edward P. Manning (2010). Using Resource-Limited Nash Memory to Improve an Othello Evaluation Function. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 2, No. 1 » Othello
 * Edward P. Manning (2010). Coevolution in a Large Search Space using Resource-limited Nash Memory. GECCO '10 » Othello
 * Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2010). A mono surrogate for multiobjective optimization. GECCO 2010, pdf
 * Gary B. Fogel, David B. Fogel, Lawrence J. Fogel (2011). Evolutionary programming. Scholarpedia, Vol. 6, No. 4
 * Borko Bošković, Janez Brest (2011). Tuning Chess Evaluation Function Parameters using Differential Evolution. Algorithm. Informatica, 35, No. 2
 * Borko Bošković, Janez Brest, Aleš Zamuda, Sašo Greiner, Viljem Žumer (2011). History mechanism supported differential evolution for chess evaluation function tuning. Soft Computing, Vol. 15, No. 4
 * Omid David, Moshe Koppel, Nathan S. Netanyahu (2011). Expert-Driven Genetic Algorithms for Simulating Evaluation Functions. Genetic Programming and Evolvable Machines, Vol. 12, No. 1, arXiv:1711.06841
 * Eduardo Vázquez-Fernández, Carlos Artemio Coello Coello, Feliú Davino Sagols Troncoso (2011). An Evolutionary Algorithm for Tuning a Chess Evaluation Function. CEC 2011, pdf
 * Eduardo Vázquez-Fernández, Carlos Artemio Coello Coello, Feliú Davino Sagols Troncoso (2011). An Adaptive Evolutionary Algorithm Based on Typical Chess Problems for Tuning a Chess Evaluation Function. GECCO 2011, pdf
 * Krzysztof Krawiec, Marcin Szubert (2011). Learning N-Tuple Networks for Othello by Coevolutionary Gradient Search. GECCO 2011, pdf
 * Krzysztof Krawiec, Wojciech Jaśkowski, Marcin Szubert (2011). Evolving small-board Go players using Coevolutionary Temporal Difference Learning with Archives. Applied Mathematics and Computer Science, Vol. 21, No. 4
 * Moshe Sipper (2011). Evolved to Win. Lulu
 * Michael Orlov, Moshe Sipper, Ami Hauptman (2012). Genetic and evolutionary algorithms and programming: General introduction and application to game playing. Computational Complexity, Springer New York
 * Kokolo Ikeda, Simon Viennot, et al. (2012). Adaptation of game AIs using Genetic Algorithm: Keeping variety and suitable strength. ISIS 2012
 * Paweł Liskowski (2012). Co-Evolution versus Evolution with Random Sampling for Acquiring Othello Position Evaluation. master's thesis, Poznań University of Technology, supervisor Wojciech Jaśkowski, pdf » Othello
 * Marcin Szubert, Krzysztof Krawiec (2012). Autonomous Shaping via Coevolutionary Selection of Training Experience. 12. PPSN
 * Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2012). Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. arXiv:1204.2356
 * Ilya Loshchilov, Marc Schoenauer, Michèle Sebag (2012). Alternative Restart Strategies for CMA-ES. arXiv:1207.0206
 * Marcin Szubert, Wojciech Jaśkowski, Krzysztof Krawiec (2013). On Scalability, Generalization, and Hybridization of Coevolutionary Learning: a Case Study for Othello. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 5, No. 3 » Othello
 * Paweł Liskowski (2013). Quantitative Analysis of the Hall of Fame Coevolutionary Archives. GECCO '13 Companion Proceedings
 * Marcin Szubert, Wojciech Jaśkowski, Paweł Liskowski, Krzysztof Krawiec (2013). Shaping Fitness Function for Evolutionary Learning of Game Strategies. GECCO 2013, pdf
 * Wojciech Jaśkowski, Paweł Liskowski, Marcin Szubert, Krzysztof Krawiec (2013). Improving Coevolution by Random Sampling. GECCO 2013, pdf
 * S. Ali Mirsoleimani, Ali Karami, Farshad Khunjush (2013). A parallel memetic algorithm on GPU to solve the task scheduling problem in heterogeneous environments. GECCO '13, pdf
 * Bartosz Wieloch (2013). Semantic Extensions for Genetic Programming. Ph.D. thesis, Poznań University of Technology, supervisor Krzysztof Krawiec, pdf
 * Ilya Loshchilov (2013). Surrogate-Assisted Evolutionary Algorithms. Ph.D. thesis, Paris-Sud 11 University, advisors Marc Schoenauer and Michèle Sebag
 * Omid David, Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu (2014). Genetic Algorithms for Evolving Computer Chess Programs. IEEE Transactions on Evolutionary Computation, arXiv:1711.08337
 * Wojciech Jaśkowski, Marcin Szubert, Paweł Liskowski (2014). Multi-Criteria Comparison of Coevolution and Temporal Difference Learning on Othello. EvoApplications 2014, Springer, volume 8602 » Othello
 * Ignacio Arnaldo, Krzysztof Krawiec, Una-May O'Reilly (2014). Multiple Regression Genetic Programming. GECCO 2014, pdf
 * Rahul Aralikatte (2014). Phoenix : A Self Learning Chess Engine. for the Award of M. Tech in Information Technology, International Institute of Information Technology, Bangalore, Bangalore, advisor G. Srinivasaraghavan, pdf

2015 ...

 * Rahul Aralikatte, G. Srinivasaraghavan (2016). Phoenix: A Self-Optimizing Chess Engine. arXiv:1603.09051
 * Ilya Loshchilov, Frank Hutter (2016). CMA-ES for Hyperparameter Optimization of Deep Neural Networks. arXiv:1604.07269
 * Wojciech Jaśkowski, Marcin Szubert (2016). Coevolutionary CMA-ES for Knowledge-Free Learning of Game Position Evaluation. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 8, No. 4
 * Wojciech Jaśkowski, Paweł Liskowski, Marcin Szubert, Krzysztof Krawiec (2016). The performance profile: A multi–criteria performance evaluation method for test–based problems. International Journal of Applied Mathematics and Computer Science, Vol. 26, No. 1

=Forum Posts=

1996 ...

 * Genetic Algorithms for Chess Evaluation Functions by Chris Mayer, rgcc, July 01, 1996
 * Re: Genetic Algorithms for Chess Evaluation Functions by Jay Scott, rgcc, July 01, 1996


 * Evolutionary Evaluation by Daniel Homan, rgcc, September 09, 1997

2010 ...

 * Revisiting GA's for tuning evaluation weights by Ilari Pihlajisto, CCC, January 03, 2010 » Automated Tuning
 * Training a Genetic algorithm? by BlueAce, OpenChess Forum, December 25, 2012
 * Chessiverse @HGM by Daniel Shawul, CCC, January 06, 2014
 * Eval tuning - any open source engines with GA or PBIL? by Hrvoje Horvatic, CCC, December 04, 2014 » Automated Tuning

2015 ...

 * Genetical tuning by Stefano Gemma, CCC, August 11, 2015 » Automated Tuning
 * Re: Genetical tuning by Ferdinand Mosca, CCC, August 20, 2015


 * Genetical learning (again) by Stefano Gemma, CCC, April 03, 2016
 * Genetic optimization re-started by Stefano Gemma, December 23, 2017

=External Links=

Genetic Programming

 * Genetic programming from Wikipedia
 * genetic-programming.org-Home-Page
 * Genetic Programming Bibliography entries for Michèle Sebag
 * Gene from Wikipedia
 * The GP Tutorial

Evolutionary Programming

 * Evolutionary programming from Wikipedia

Genetic Algorithms

 * Genetic algorithms
 * Genetic algorithms from Wikipedia
 * Genetic representation from Wikipedia
 * Fitness function from Wikipedia
 * Genetic operator from Wikipedia
 * Selection (genetic algorithm) from Wikipedia
 * Fitness proportionate selection from Wikipedia
 * Reward-based selection from Wikipedia
 * Stochastic universal sampling from Wikipedia
 * Tournament selection from Wikipedia
 * Truncation selection from Wikipedia


 * Crossover (genetic algorithm) from Wikipedia
 * Population-based incremental learning from Wikipedia
 * llinois Genetic Algorithms Lab | Life, Liberty, and the Pursuit of Genetic Algorithm
 * Genetic Algorithms from Articles On Artificial Intelligence

Evolutionary Algorithms

 * Evolutionary algorithms from Wikipedia
 * Evolutionary programming from Wikipedia
 * Evolution strategy from Wikipedia
 * CMA-ES from Wikipedia
 * The Chessiverse: Evolution of Chess Programs by Harm Geert Muller

Evolutionary Computation

 * Evolutionary computation from Wikipedia
 * Computational intelligence from Wikipedia
 * Biological evolution from Wikipedia
 * Symbiogenesis from Wikipedia
 * Category: Biological evolution from Wikipedia
 * Differential evolution from Wikipedia
 * Memetic algorithms from Wikipedia

Misc

 * Optimization from Wikipedia
 * Simulated annealing from Wikipedia
 * Artificial life from Wikipedia
 * Digital organism from Wikipedia
 * Survival of the fittest from Wikipedia
 * The Headhunters - If You Got It, You'll Get It, Survival of the Fittest, Winterland, May 09, 1975, YouTube Video
 * feat.: Bennie Maupin, Bill Summers, Paul Jackson, Mike Clark and Blackbird McKnight

=References= Up one Level