TY - GEN
T1 - Using adaptive novelty search in differential evolution
AU - Fister, Iztok
AU - Iglesias, Andres
AU - Galvez, Akemi
AU - Del Ser, Javier
AU - Osaba, Eneko
AU - Fister, Iztok
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Novelty search ensures evaluation of solutions in stochastic population-based nature-inspired algorithms according to additional measure, where each solution is evaluated by a distance to its neighborhood beside the fitness function. Thus, the population diversity is preserved that is a prerequisite for the open-ended evolution in evolutionary robotics. Recently, the Novelty search was applied for solving the global optimization into differential evolution, where all Novelty search parameters remain unchanged during the run. The novelty area width parameter, that determines the diameter specifying the minimum change in each direction needed the solution for treating as the novelty, has a crucial influence on the optimization results. In this study, this parameter was adapted during the evolutionary process. The proposed self-adaptive differential evolution using the adaptive Novelty search were applied for solving the CEC 2014 Benchmark function suite, and the obtained results confirmed the usefulness of the adaptation.
AB - Novelty search ensures evaluation of solutions in stochastic population-based nature-inspired algorithms according to additional measure, where each solution is evaluated by a distance to its neighborhood beside the fitness function. Thus, the population diversity is preserved that is a prerequisite for the open-ended evolution in evolutionary robotics. Recently, the Novelty search was applied for solving the global optimization into differential evolution, where all Novelty search parameters remain unchanged during the run. The novelty area width parameter, that determines the diameter specifying the minimum change in each direction needed the solution for treating as the novelty, has a crucial influence on the optimization results. In this study, this parameter was adapted during the evolutionary process. The proposed self-adaptive differential evolution using the adaptive Novelty search were applied for solving the CEC 2014 Benchmark function suite, and the obtained results confirmed the usefulness of the adaptation.
KW - Adaptive Novelty search
KW - Differential evolution
KW - Evolutionary robotics
KW - Open-ended evolution
UR - http://www.scopus.com/inward/record.url?scp=85068623587&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-24299-2_23
DO - 10.1007/978-3-030-24299-2_23
M3 - Conference contribution
AN - SCOPUS:85068623587
SN - 9783030242985
T3 - Communications in Computer and Information Science
SP - 267
EP - 275
BT - Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection - International Workshops of PAAMS 2019, Proceedings
A2 - De La Prieta, Fernando
A2 - González-Briones, Alfonso
A2 - Pawleski, Pawel
A2 - Calvaresi, Davide
A2 - Del Val, Elena
A2 - Julian, Vicente
A2 - Lopes, Fernando
A2 - Osaba, Eneko
A2 - Sánchez-Iborra, Ramón
PB - Springer Verlag
T2 - 17th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2019
Y2 - 26 June 2019 through 28 June 2019
ER -