designing a new structure based on learning automaton to improve evolutionary algorithms (with considering some case study problems)

evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharply and the algorithm is likely to converge on best results very quickly.this paper contributes an algorithm based on learning automaton to improve theevolutionary algorithm for solving a group of np problems. it uses concepts ofmachine learning in search process, and increases efficiency of evolutionaryalgorithm (especially genetic algorithm). in fact, the algorithm is prevented frombeing stuck in local optimal solutions by using learning automaton. another positivepoint of the hybrid algorithm is its noticeable stability since standard division ofresults, which is obtained by different executions of algorithm, is low; that is, theresults are practically the same. therefore, as the proposed algorithm is used for aset of well-known np problems and the results are very suitable it can be consideredas a precise and reliable technique to solve the problems.