遺伝的アルゴリズム 情報リソース

書籍

• メタヒューリスティクスの数理 : Pythonコードあり。多目的GAなし
• メタヒューリスティクスと応用 : 480頁。
• 実践 遺伝的アルゴリズム : 多目的GAあり。Javaコード

ツール

Platypus : NSGAIIによる多目的GA

• https://platypus.readthedocs.io/en/latest/getting-started.html

DEAP

• https://github.com/lmarti/evolutionary-computation-course/blob/master/AEC.06%20-%20Evolutionary%20Multi-Objective%20Optimization.ipynb

論文

A fast and elitist multiobjective genetic algorithm: NSGA-II（2005）

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.