ABSTRACT
Lamarckian inheritance of acquired characteristics maintains that the acquired development of strong legs, through years of exercise in a mountainous environment, will influence the actual genetic makeup of the horse. Lamarckian localized optimization will typically boost the relative fitness of the population, and hence accelerate search performance of the parent genetic algorithm. Lamarckian evolution can be circumvented if an optimal individual replaces another weaker individual in the population, using an appropriate selection criterion. The practicality of Lamarckian evolution within a genetic algorithm depends upon the problem being investigated. The Traveling Salesman Problem (TSP) is a NP-complete problem that has attracted much attention in the theoretical computer science and genetic algorithm communities. The TSP is to determine a tour order for the cities such that all the cities are visited, the tour finishes where it began, and the overall tour distance is minimal.
