A Parallel Memetic Algorithm for Solving Optimization Problems
Jason G. Digalakis, Konstantinos G. Margaritis · 2001
Introduction Optimization algorithms can be classified in heuristic and metaheuristic. In the class of heuristic algorithms, there are construction and improvement algorithms. Metaheuristic algorithms may manage a chain or flow of executions of classical heuristics, e.g. Tabu Search, Simulated Annealing, Genetic and Memetic Algorithms. MAs[12, 13, 10] are population-based heuristic search approaches for optimization problems similar to genetic algorithms (GAs). GAs, however, rely on the concept of biological evolution, but MAs, in contrast, mimic cultural evolution. In this paper we examine 10 di#erent functions in order (a) to test specific parameter of a parallel execution of memetic algorithms and (b) to evaluate the general computational behavior of MAs. The available theoretical analysis on memetic algorithms does not o#er a tool which could help in a generalized adjustment of control parameters, leaving the choice of the proper operators, parameters and mechanisms to d