Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

TL;DR

This paper introduces the Genetic Engineering Algorithm (GEA), a novel metaheuristic inspired by genetic engineering, designed to improve the efficiency and effectiveness of solving combinatorial optimization problems compared to traditional genetic algorithms.

Contribution

The paper presents GEA, a new algorithm that enhances traditional genetic algorithms with genetic engineering concepts, leading to better performance on benchmark problems.

Findings

GEA outperforms state-of-the-art algorithms on benchmark instances.

GEA effectively isolates, purifies, inserts, and expresses genes to produce optimal solutions.

The algorithm demonstrates superior convergence and solution quality.

Abstract

Genetic Algorithms (GAs) are known for their efficiency in solving combinatorial optimization problems, thanks to their ability to explore diverse solution spaces, handle various representations, exploit parallelism, preserve good solutions, adapt to changing dynamics, handle combinatorial diversity, and provide heuristic search. However, limitations such as premature convergence, lack of problem-specific knowledge, and randomness of crossover and mutation operators make GAs generally inefficient in finding an optimal solution. To address these limitations, this paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts. GEA redesigns the traditional GA while incorporating new search methods to isolate, purify, insert, and express new genes based on existing ones, leading to the emergence of desired traits and the production of specific chromosomes based on the selected genes. Comparative evaluations against state-of-the-art algorithms on benchmark instances demonstrate the superior performance of GEA, showcasing its potential as an innovative and efficient solution for combinatorial optimization problems.