Memetic Algorithms in Constrained Optimization
Memetic Algorithms (MAs) are a fairly recent breed of optimization algorithms created through a synergetic coupling of global and local search strategies .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 . 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 . The exploitation schemes adopted in MAs include incorporation of heuristics, approximation algorithms, local search algorithms, specialized schemes for recombination etc.
KeywordsLocal Search Partial Reordering Constrain Optimization Problem Sequential Quadratic Program Memetic Algorithm
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