HEMO: A Sustainable Multi-objective Evolutionary Optimization Framework

The capability of multi-objective evolutionary algorithms (MOEAs) to handle premature convergence is critically important when applied to real-world problems. Their highly multi-modal and discrete search space often makes the required performance out of reach to current MOEAs. Examining the fundamental cause of premature convergence in evolutionary search has led to proposing of a generic framework, called HEMO, for evolutionary multi-objective optimization. HEMO is characterized by its simultaneous maintenance of individuals of all degrees of evolution in hierarchically organized repositories, by its continuous inflow of random individuals at the base repository, by its intrinsic hierarchical elitism and hyper-grid-based density estimation. Two experiments demonstrate its search robustness and its capability to provide sustainable evolutionary search for difficult multi-modal problems. HEMO makes it possible to do reliable multi-objective search without risk of premature convergence. ...

• Type: Conference
• Year: 2003
• Where: GECCO
• Authors: Jianjun Hu, Kisung Seo, Zhun Fan, Ronald C. Rosenberg, Erik D. Goodman
• Keywords: Evolutionary Search, GECCO 2003, Multi-objective Evolutionary Algorithms, Premature Convergence