Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
A Comparison Of Two Competitive Fitness Functions
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
GP-Gammon: Genetically Programming Backgammon Players
Genetic Programming and Evolvable Machines
Emergent geometric organization and informative dimensions in coevolutionary algorithms
Emergent geometric organization and informative dimensions in coevolutionary algorithms
Winning ant wars: evolving a human-competitive game strategy using fitnessless selection
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Evolving strategy for a probabilistic game of imperfect information using genetic programming
Genetic Programming and Evolvable Machines
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We introduce fitnessless coevolution (FC), a novel method of comparative one-population coevolution. FC plays games between individuals to settle tournaments in the selection phase and skips the typical phase of evaluation. The selection operator applies a single-elimination tournament to a randomly drawn group of individuals, and the winner of the final round becomes the result of selection. Therefore, FC does not involve explicit fitness measure. We prove that, under a condition of transitivity of the payoff matrix, the dynamics of FC is identical to that of the traditional evolutionary algorithm. The experimental results, obtained on a diversified group of problems, demonstrate that FC is able to produce solutions that are equally good or better than solutions obtained using fitness-based one-population coevolution with different selection methods.