Title

Landscape synergy in evolutionary multitasking

Publication Type

Conference Proceeding Article

Publication Date

7-2016

Abstract

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.

Keywords

Evolutionary Multitasking, Evolutionary Optimization, Landscape Synergy, Memetic Computation

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Decision Analytics

Publication

CEC 2016: Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver; July 24-29

First Page

3076

Last Page

3083

ISBN

9781509006229

Identifier

10.1109/CEC.2016.7744178

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://doi.org/10.1109/CEC.2016.7744178

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