Algorithm portfolio for individual-based surrogate-assisted evolutionary algorithms

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems.

Chips-n-Salsa: A Java Library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms

“…It also enables easily integrating new mutation operators, and customizing all stages of the local search (e.g., choice of annealing schedule for simulated annealing). • Hybridizable: Chips-n-Salsa supports integrating multiple forms of local search (e.g., hybrids of hill climbing with simulated annealing), creating hybrid mutation operators (e.g., combining multiple mutation operators), and running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio (Gomes & Selman, 2001;Tong, Liu, & Yao, 2019). • Iterative: Chips-n-Salsa supports multistart metaheuristics, including several restart schedules (Cicirello, 2017;Luby, Sinclair, & Zuckerman, 1993) for varying the run lengths across the restarts.…”