手法を比較

	遺伝的アルゴリズム	Harmony Search	焼きなまし法
分野	最適化	最適化	最適化
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1975	2001	1983
提唱者	John Henry Holland	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan	—
種類	Population-based metaheuristic	Metaheuristic population-based optimization	Probabilistic metaheuristic / local search
原典	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68.	Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
別名	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
関連	5	5	5
概要	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	Harmony Search (HS) is a population-based metaheuristic optimization algorithm introduced by Geem, Kim, and Loganathan in 2001. It mimics the improvisation process of jazz musicians seeking a perfect state of harmony, using three operators — memory consideration, pitch adjustment, and random selection — to generate candidate solutions. The algorithm applies to both continuous and discrete variables and has found wide use in engineering design, water distribution network optimization, and combinatorial problems.	Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.