Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms

The foundations for evolutionary algorithms (EAs) were established in the end of the 60’s [1, 2] (EAs) and strengthened in the beginning of the 70’s [3, 4]. EAs appeared as an alternative to the exact or approximate optimization methods whose application to many real problems were not acceptable in terms of performance. When applied to real problems, EAs provide a valuable relation between quality of the solution and efficiency to obtain it; for this reason these techniques attracted immediately the attention of many researchers and became what they nowadays represent: the cutting-edge approach to real-world optimization. Certainly, this has also been the case for other related techniques, such as simulated annealing [5] (SA), tabu search [6] (TS), etc. The term metaheuristics has been coined to denote them. The term hybrid evolutionary algorithm (HEAs) (resp. hybrid metaheuristics) refers to the combination of an evolutionary technique (resp. metaheuristics) with 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 each of the involved components working alone. HEAs have been proved to be very successful in the optimization of many practical problems (e.g., [7, 8]) and, as a consequence, currently there exist an increasing interest in the optimization community for this kind of techniques. One crucial point in the the development of HEAs (and hybrid metaheuristics in general) is the need of exploiting problem knowledge as was clearly exposed in the formulation of the No Free Lunch Theorem (NFL) by Wolpert and Macready [9] (a search algorithm performs in strict accordance with the amount and quality of the problem knowledge they incorporate). Quite interestingly, this line of thinking had