Memetic Algorithms in Constrained Optimization

Abstract

Memetic Algorithms (MAs) are a fairly recent breed of optimization algorithms created through a synergetic coupling of global and local search strategies [615].While predecessors of MAs, i.e. Genetic Algorithms (GAs) and Evolutionary Algorithms (EAs) have had significant success in solving a number of real life complex optimization problems in the past, their performance can be greatly improved though a hybridization with other techniques [188]. GAs or EAs hybridized with local search strategies are commonly referred as memetic algorithms. These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. While, the underlying GA/EA provides the ability for exploration, the local search aids in exploitation [492]. The exploitation schemes adopted in MAs include incorporation of heuristics, approximation algorithms, local search algorithms, specialized schemes for recombination etc.