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In this thesis we propose new methods for crossover operator namely: cut onworst gene (COWGC), cut on worst L+R gene (COWLRGC) and Collision Crossovers.And also we propose several types of mutation operator such as: worst gene withrandom gene mutation (WGWRGM) , worst LR gene with random gene mutation(WLRGWRGM), worst gene with worst gene mutation (WGWWGM), worst gene withnearest neighbour mutation (WGWNNM), worst gene with the worst around thenearest neighbour mutation (WGWWNNM), worst gene inserted beside nearestneighbour mutation (WGIBNNM), random gene inserted beside nearest neighbourmutation (RGIBNNM), Swap worst gene locally mutation (SWGLM), Insert bestrandom gene before worst gene mutation (IBRGBWGM) and Insert best random genebefore random gene mutation (IBRGBRGM). In addition to proposing four selectionstrategies, namely: select any crossover (SAC), select any mutation (SAM),select best crossover (SBC) and select best mutation (SBM). The first two arebased on selection of the best crossover and mutation operator respectively,and the other two strategies randomly select any operator. So we investigatethe use of more than one crossover/mutation operator (based on the proposedstrategies) to enhance the performance of genetic algorithms. Our experiments,conducted on several Travelling Salesman Problems (TSP), show the superiorityof some of the proposed methods in crossover and mutation over some of thewell-known crossover and mutation operators described in the literature. Inaddition, using any of the four strategies (SAC, SAM, SBC and SBM), found to bebetter than using one crossover/mutation operator in general, because thoseallow the GA to avoid local optima, or the so-called premature convergence.Keywords: GAs, Collision crossover, Multi crossovers, Multi mutations, TSP.

Novel Methods for Enhancing the Performance of Genetic Algorithms