Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.

Abstract-This paper presents a novel use of Genetic Programming, Co-Evolution and Interactive Fitness to evolve algorithms for the game of Tic-Tac-Toe. The selected tree-structured algorithms are evaluated based on a fitness-less double-game strategy and then compete against a human player. This paper will outline the evolution process which leads to producing the best Tic-Tac-Toe playing algorithm. The evolved algorithms have proven effective for playing against human opponents.

Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a novel method for adaptively tuning the genome size is presented. The approach introduces new mutation, transposition and recolI)bination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations and enhances parallel GEP. To test our approach an assortment of problems were used, including symbolic regression, classification and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptively tuning the genome size of GEP's representation.

Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a novel method for adaptively tuning the genome size is presented. The approach introduces new mutation, transposition and recolI)bination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations and enhances parallel GEP. To test our approach an assortment of problems were used, including symbolic regression, classification and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptively tuning the genome size of GEP's representation.