A genetic local tuning algorithm for a class of combinatorialnetworks design problems

Experimental evidences of many genetic algorithm (GA) researchers is that hybridizing a GA with a local search (LS) heuristic is beneficial. It combines the ability of the GA to widely sample a search space with a local search hill-climbing ability. This letter presents a genetic local search (GALS) mechanism applied on two stages on the initial genetic population. An elite nondominated set of solutions is selected, an intermediate population (IP) composed of the elite and the improved solutions by natural genetic operators is constructed and then a Nelder and Mead (1965) simplex downhill method (SDM) is applied to some solutions of the IP. Experimental results from solving a 20-nodes topology design and capacity assignment (TDCA) problem suggest that our approach provides superior results compared to four simple GA implementations found in the literature