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.

翻译：遗传算法因其在探索多样化解空间、处理多种表示形式、利用并行性、保留优良解、适应动态变化、处理组合多样性以及提供启发式搜索等方面的能力，在解决组合优化问题方面以高效著称。然而，其局限性如早熟收敛、缺乏问题特定知识以及交叉和变异算子的随机性，导致遗传算法在寻找最优解时通常效率不高。为了解决这些局限性，本文提出了一种新的元启发式算法——遗传工程设计算法(GEA)，该算法从遗传工程概念中汲取灵感。GEA重新设计了传统遗传算法，同时融入了新的搜索方法，以基于现有基因分离、纯化、插入和表达新基因，从而促使期望性状的出现，并基于所选基因产生特定染色体。在基准实例上与最先进算法的对比评估表明，GEA展现了卓越的性能，彰显其作为解决组合优化问题的创新且高效方案的潜力。