ABSTRACT
This chapter provides brief introduction to genetic algorithms (GA) and describes the proposed fitness evaluation methodology. A new fitness evaluation criterion for GAs is introduced where the fitness value of an individual is determined by considering its own fitness as well as that of its ancestors. The guidelines for selecting the weighting coefficients, both heuristically and automatically, which quantify the importance to be given on the fitness of the individual and its ancestors, are provided. GAs are adaptive and robust computational procedures modeled on the mechanics of natural genetic systems. GAs act as a biological metaphor and try to emulate some of the processes observed in natural evolution. A GA typically consists of the following components: A population of strings or coded possible solutions, a mechanism to encode a possible solution, Objective function and associated fitness evaluation techniques, Selection/reproduction procedure, Genetic operators, Probabilities to perform genetic operations.
