RT Conference Proceedings
T1 Multifactorial Cellular Genetic Algorithm (MFCGA): 												Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
A1 Osaba, Eneko
A1 Martinez, Aritz D.
A1 Lobo, Jesus L.
A1 Ser, Javier Del
A1 Herrera, Francisco
AB Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergies between the problems (tasks) to be optimized, helping each other via knowledge transfer (thereby being referred to as Transfer Optimization). Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration. As such, EM approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has shown a remarkable success when dealing with multiple discrete, continuous, single-, and/or multi-objective optimization problems. In this work we propose a novel algorithmic scheme for Multifactorial Optimization scenarios - the Multifactorial Cellular Genetic Algorithm (MFCGA) - that hinges on concepts from Cellular Automata to implement mechanisms for exchanging knowledge among problems. We conduct an extensive performance analysis of the proposed MFCGA and compare it to the canonical MFEA under the same algorithmic conditions and over 15 different multitasking setups (encompassing different reference instances of the discrete Traveling Salesman Problem). A further contribution of this analysis beyond performance benchmarking is a quantitative examination of the genetic transferability among the problem instances, eliciting an empirical demonstration of the synergies emerged between the different optimization tasks along the MFCGA search process.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781728169293
YR 2020
FD 2020-07
LK https://hdl.handle.net/11556/2305
UL https://hdl.handle.net/11556/2305
LA eng
NO Osaba , E , Martinez , A D , Lobo , J L , Ser , J D & Herrera , F 2020 , Multifactorial Cellular Genetic Algorithm (MFCGA) : Algorithmic Design, Performance Comparison and Genetic Transferability Analysis . in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings . , 9185784 , 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings , Institute of Electrical and Electronics Engineers Inc. , 2020 IEEE Congress on Evolutionary Computation, CEC 2020 , Virtual, Glasgow , United Kingdom , 19/07/20 . https://doi.org/10.1109/CEC48606.2020.9185784
NO conference
NO Publisher Copyright: © 2020 IEEE.
NO Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo and Javier Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs. Javier Del Ser receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government.
DS TECNALIA Publications
RD 28 jul 2024