A Dispersion Operator for Geometric Semantic Genetic Programming

Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. However, there are still many open issues on how to improve their search mechanism. One of these issues is how to get around the fact that the GSGP crossover operator cannot generate solutions that are placed outside the convex hull formed by the individuals of the current population. Although the mutation operator alleviates this problem, we cannot guarantee it will find promising regions of the search space within feasible computational time. In this direction, this paper proposes a new geometric dispersion operator that uses multiplicative factors to move individuals to less dense areas of the search space around the target solution before applying semantic genetic operators. Experiments in sixteen datasets show that the results obtained by the proposed operator are statistically significantly better than those produced by GSGP and that the operator does indeed spread the solutions around the target solution

A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction

This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization

Genetic algorithm design of neural network and fuzzy logic controllers

Genetic algorithm design of neural network and fuzzy logic controller

Self-adaptation of Genetic Operators Through Genetic Programming Techniques

Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.Comment: Presented in GECCO 201