Landscape synergy in evolutionary multitasking

• Main Authors: GUPTA, Abhishek, ONG, Yew Soon, DA, B., Stephanus Daniel, Handoko, HANDOKO, Stephanus D.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Evolutionary Multitasking; Evolutionary Optimization; Landscape Synergy; Memetic Computation; Computer Sciences; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/3623
• Institution: Singapore Management University

Over the years, the algorithms of evolutionary computation have emerged as popular tools for tackling complex real-world optimization problems. A common feature among these algorithms is that they focus on efficiently solving a single problem at a time. Despite the availability of a population of individuals navigating the search space, and the implicit parallelism of their collective behavior, seldom has an effort been made to multitask. Considering the power of implicit parallelism, we are drawn to the idea that population-based search strategies provide an idyllic setting for leveraging the underlying synergies between objective function landscapes of seemingly distinct optimization tasks, particularly when they are solved together with a single population of evolving individuals. As has been recently demonstrated, allowing the principles of evolution to autonomously exploit the available synergies can often lead to accelerated convergence for otherwise complex optimization tasks. With the aim of providing deeper insight into the processes of evolutionary multitasking, we present in this paper a conceptualization of what, in our opinion, is one possible interpretation of the complementarity between optimization tasks. In particular, we propose a synergy metric that captures the correlation between objective function landscapes of distinct tasks placed in synthetic multitasking environments. In the long run, it is contended that the metric will serve as an important guide toward better understanding of evolutionary multitasking, thereby facilitating the design of improved multitasking engines.