Combining meta-heuristics with local search methods is one approach that
recently has drawn much attention to design more efficient methods. In this
paper, a new algorithm called Simplex Coding Genetic Algorithm (SCGA) is
proposed by hybridizing genetic algorithm and simplex-based local search
method called Nelder-Mead method. In the SCGA, each chromosome in the
population is a simplex and the gene is a vertex of this simplex. Selection,
new multi-parents crossover and mutation procedures are used to improve the
initial population. Moreover, Nelder-Mead method is applied to improve the
population in the initial stage and every intermediate stage when new
children are generated. Applying Nelder-Mead method again on the best point
visited is the final stage in the SCGA to accelerate the search and to
improve this best point. The efficiency of SCGA is tested on some well known
functions. Comparison with other meta-heuristics indicates that the SCGA is
promising.