Many optimization problems require the satisfaction of constraints in addition to their objectives. When using an evolutionary algorithm to solve such problems, these constraints c...
High–dimensional optimization problems appear very often in demanding applications. Although evolutionary algorithms constitute a valuable tool for solving such problems, their ...
We introduce a new approach to GA (Genetic Algorithms) based problem solving. Earlier GAs did not contain local search (i.e. hill climbing) mechanisms, which led to optimization d...
Hitoshi Iba, Tetsuya Higuchi, Hugo de Garis, Taisu...
In this work we present a novel and efficient algorithm– independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorit...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...