Logic gate-based evolutionary algorithm for the multidimensional knapsack problem

Abstract

Evolutionary algorithms (EAs) are powerful techniques for solving continuous optimization problems in different domains. However, research on the binary form of the EAs is currently not massive. In this paper, a logic gate-based evolutionary algorithm (LGEA) is introduced. The proposed LGEA has the following features. First, it replaces common space transformation rules and classic recombination and mutation methods by the logic operation. Second, it is based on exploiting various logic gates to find the best solution. The variety among these logic tools will eventually lead to intensify diversity in the search space and enhance global search abilities. Thereby, the LGEA represents a new technique to combine the logic gates into the process of producing offspring in an evolutionary context. To evaluate the performance of the algorithm, we have solved the NP-hard multidimensional knapsack problem. Experimental results show that the proposed LGEA is promising.