  In this thesis we propose new methods for crossover operator namely: cut on
worst 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 with
random gene mutation (WGWRGM) , worst LR gene with random gene mutation
(WLRGWRGM), worst gene with worst gene mutation (WGWWGM), worst gene with
nearest neighbour mutation (WGWNNM), worst gene with the worst around the
nearest neighbour mutation (WGWWNNM), worst gene inserted beside nearest
neighbour mutation (WGIBNNM), random gene inserted beside nearest neighbour
mutation (RGIBNNM), Swap worst gene locally mutation (SWGLM), Insert best
random gene before worst gene mutation (IBRGBWGM) and Insert best random gene
before random gene mutation (IBRGBRGM). In addition to proposing four selection
strategies, namely: select any crossover (SAC), select any mutation (SAM),
select best crossover (SBC) and select best mutation (SBM). The first two are
based on selection of the best crossover and mutation operator respectively,
and the other two strategies randomly select any operator. So we investigate
the use of more than one crossover/mutation operator (based on the proposed
strategies) to enhance the performance of genetic algorithms. Our experiments,
conducted on several Travelling Salesman Problems (TSP), show the superiority
of some of the proposed methods in crossover and mutation over some of the
well-known crossover and mutation operators described in the literature. In
addition, using any of the four strategies (SAC, SAM, SBC and SBM), found to be
better than using one crossover/mutation operator in general, because those
allow the GA to avoid local optima, or the so-called premature convergence.
Keywords: GAs, Collision crossover, Multi crossovers, Multi mutations, TSP.
