Abstract
As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The chi ^2 goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.
Highlights
Several modern meta-heuristic algorithms have been developed during the last five decades for solving the nondeterministic polynomial (NP-hard) and complex nature optimization problems
The fitness proportional selection approach has essence exploitation and linear rank approach is influenced by exploration
This article presented a new split ranked selection operator which is a great trade-off between exploration and exploitation
Summary
Several modern meta-heuristic algorithms have been developed during the last five decades for solving the nondeterministic polynomial (NP-hard) and complex nature optimization problems. The first operator is selection being used to choose the set of chromosomes for mating process, the crossover is the second one and used to create new individuals, and the last one is the mutation used for random changes. GA is one of those algorithms whose performance is highly affected by the choice of selection operator Without this mechanism, GA is only simple random sampling giving different results in each generation. The main objective of this study is to present the performance of selection operators that have a major impact on the GAs process In this way, a new selection operator is proposed that intended to enhance the average quality of the population and gives a better trade-off between exploration and exploitation. Performance evaluation of the proposed scheme and conclusions are given in “Performance evaluation” and “Conclusions”, respectively
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