Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II

Abstract

This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve optimization problems via exploiting problem substructures. The proposed algorithm adopts pairwise linkage detection and stores the information in the form of dependency structure matrix (DSM). A new linkage model, called the incremental linkage set, is then constructed by using the DSM. Inspired by the idea of optimal mixing, the restricted mixing and the back mixing are proposed. The former aims at efficient exploration under certain constrains. The latter aims at exploitation by refining the DSM so as to reduce unnecessary evaluations. Experimental results show that DSMGA-II outperforms LT-GOMEA and hBOA in terms of number of function evaluations on the concatenated/folded/cyclic trap problems, NK-landscape problems with various degrees of overlapping, 2D Ising spin-glass problems, and MAX-SAT. The investigation of performance comparison with P3 is also included.

Genetic and Evolutionary Computation Conference (GECCO'15)

Cited by in corpus (4)

• Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions
• Deep Optimisation: Solving Combinatorial Optimisation Problems using Deep Neural Networks
• The Pitfalls and Potentials of Adding Gene-invariance to Optimal Mixing
• Moving between high-quality optima using multi-satisfiability characteristics in hard-to-solve Max3Sat instances