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.

提出了一种基于遗传工程概念的新元启发算法 —— 遗传工程算法（GEA），通过重新设计传统遗传算法并引入新的搜索方法，以基于已有基因的选择来孤立、提纯、插入和表达新基因，从而产生具有所选基因的特定染色体，旨在解决遗传算法中的限制问题。与现有算法在基准实例上进行的比较评估表明，GEA 具有卓越的性能，展示了其作为组合优化问题的创新高效解决方案的潜力。