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Genetic Programming (GP),
is 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.
Genetic Programming [1]

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)
    1. Fitness proportionate selection
    2. Reward-based selection
    3. Stochastic universal sampling
    4. Tournament selection
    5. Truncation selection
  2. 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 [2]. The algorithm is simpler than a standard genetic algorithm, and in many cases leads to better results than a standard genetic algorithm [3].

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


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 ...

1970 ...

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

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  • Ryszard Michalski (2000). LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning. Machine Learning 38 [5]
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Forum Posts

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External Links


References

  1. ^ genetic-programming.org-Home-Page
  2. ^ 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)
  3. ^ Population-based incremental learning from Wikipedia
  4. ^ Prisoner's dilemma from Wikipedia
  5. ^ Learnable Evolution Model from Wikipedia
  6. ^ Dap Hartmann (2010). Mimicking the Black Box - Genetically evolving evaluation functions and search algorithms. Review on Omid David's Ph.D. Thesis, ICGA Journal, Vol. 33, No. 1
  7. ^ Jaap van den Herik wint Humies Award 2014 - LIACS - Leiden Institute of Advanced Computer Science
  8. ^ GECCO 2014
  9. ^ Re: PHOENIX=CuckooChess with learning function in Falcon style by Peter Österlund, CCC, March 20, 2016
  10. ^ The Chessiverse: Evolution of Chess Programs by Harm Geert Muller
  11. ^ Chessiverse @HGM by Daniel Shawul, CCC, January 06, 2014

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