caa a22 4500 605468524 CHVBK 20210128100317.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1309-6 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1309-6 Fieldsend Jonathan Computer Science, University of Exeter, EX4 4QF, Exeter, UK aut Using an adaptive collection of local evolutionary algorithms for multi-modal problems [Elektronische Daten] [Jonathan Fieldsend] Multi-modality can cause serious problems for many optimisers, often resulting convergence to sub-optimal modes. Even when this is not the case, it is often useful to locate and memorise a range of modes in the design space. This is because "optimal” decision parameter combinations may not actually be feasible when moving from a mathematical model emulating the real problem, to engineering an actual solution, making a range of disparate modal solutions of practical use. This paper builds upon our work on the use of a collection of localised search algorithms for niche/mode discovery which we presented at UKCI 2013 when using a collection of surrogate models to guide mode search. Here we present the results of using a collection of exploitative local evolutionary algorithms (EAs) within the same general framework. The algorithm dynamically adjusts its population size according to the number of regions it encounters that it believes contain a mode and uses localised EAs to guide the mode exploitation. We find that using a collection of localised EAs, which have limited communication with each other, produces competitive results with the current state-of-the-art multi-modal optimisation approaches on the CEC 2013 benchmark functions. Springer-Verlag Berlin Heidelberg, 2014 Evolutionary algorithms nationallicence Multi-modal problems nationallicence Local search nationallicence Dynamic populations nationallicence Self-adaptation nationallicence Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1445-1460 1432-7643 19:6<1445 2015 19 500 https://doi.org/10.1007/s00500-014-1309-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1309-6 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Fieldsend Jonathan Computer Science, University of Exeter, EX4 4QF, Exeter, UK aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1445-1460 1432-7643 19:6<1445 2015 19 500