A new deterministic greedy genetic algorithm selection operator with very high selection pressure, dubbed the “Jugate Adaptive Method” is examined. Its performance and behavior are compared to those of a canonical genetic algorithm with tournament selection, and a random-restarting next-ascent stochastic hill-climber. All three algorithms are tuned using parameter sweeps to optimize their success rates on five combinatorial optimization problems, tuning each algorithm for each problem independently. Results were negative in that the new method was outperformed in nearly all experiments. Experimental data show the hill climber to be the clear winner in four of five test problems.
['Lee Graham', 'John Borbone', 'Gary B. Parker']
Comparison of a greedy selection operator to tournament selection and a hill climber