Simple Distributed Bit Climber for Many-objective Optimization of Binary Epistatic Problems
Yudai Tagawa, Hernan E. Aguirre, Kiyoshi Tanaka · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024
We study a distributed bit climbing algorithm for many-objective optimization of binary epistatic problems. This algorithm decomposes the many-objective problem into a minimum number of single-objective problems, specified by the original evaluation functions and one additional scalarizing function that computes the solution hypervolume. Random bit climbers optimize separately the single-objective functions until they reach a local optimum and restart their search from a bounded population of non-dominated solutions collected from the solutions generated by all climbers. In this work, we study the effects on search performance of bounding the non-dominated population to support the search, two methods to truncate the population, one based on crowding distance and the other one on weighted sum scalarizing functions. Also, we study the importance of the solution hypervolume as a scalarizing function to explore the central regions of objective space. We evaluate the method on subclasses of epistatic problems using MNK-landscapes and compare with MOEA/D and NSGA-III, showing that the simpler distributed bit climber is superior to the other MOEAs in all but one subclass of problems.