C. COTTA and A. J. FERNÁNDEZ
Universidad de Málaga, Spain
The foundations for evolutionary algorithms (EAs) were established at the end of the 1960s [1,2] and strengthened at the beginning of the 1970s [3,4]. EAs appeared as an alternative to exact or approximate optimization methods whose performance in many real problems was not acceptable. When applied to real problems, EAs provide a valuable relation between the quality of the solution and the efficiency in obtaining it; for this reason these techniques attracted the immediate attention of many researchers and became what they now represent: a cutting-edge approach to real-world optimization. Certainly, this has also been the case for related techniques such as simulated annealing (SA)  and tabu search (TS) . The term metaheuristics has been coined to denote them.
The term hybrid evolutionary algorithm (HEA) or hybrid metaheuristics refers to the combination of an evolutionary technique (i.e., metaheuristics) and another (perhaps exact or approximate) technique for optimization. The aim is to combine the best of both worlds, with the objective of producing better results than can be obtained by any component working alone. HEAs have proved to be very successful in the optimization of many practical problems [7,8], and ...