An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation

Abstract

Binary tournament (BT) selection is known as an established selection operator that has been employed in various problems. However, in the development of evolutionary algorithms (EA), this selection operator has a drawback in providing an efficient implementation of the union procedure, which cannot guarantee a parsimonious knowledge base with reduced number of rules. Therefore, this paper introduces binary-standard deviation (SD) tournament selection into EA as an enhancement of BT that can lead to focus on more exploration in terms of searching for the best solutions. The proposed selection operator has been experimented within fish feed formulation in grouper fish farming as a case study on finding the minimum cost and fulfilling constraints. This approach is better than experimental design in terms of costs and time. The motivation for doing so is to search for alternative ingredients for the grouper fish, as the price of trash fish is too luxurious. It is because grouper fish are carnivorous and need many trash fish for better growth. The novelty of the proposed SD tournament selection is compared with BT selection in terms of searching for an efficient but not myopic algorithm. Hence, based on the comparative study, the findings of the enhanced selection operator towards the EA have been convinced and accepted in terms of better cost and fulfilling constraints requirements.

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