- Title
- Memetic algorithms
- Creator
- Cotta, Carlos; Mathieson, Luke; Moscato, Pablo
- Relation
- ARC.FT120100060 http://purl.org/au-research/grants/arc/FT120100060
- Relation
- Handbook of Heuristics p. 607-638
- Publisher Link
- http://dx.doi.org/10.1007/978-3-319-07124-4_29
- Publisher
- Springer
- Resource Type
- book chapter
- Date
- 2018
- Description
- Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.
- Subject
- evolutionary algorithms; hybridization; local search; memetic computing; metaheuristics
- Identifier
- http://hdl.handle.net/1959.13/1404051
- Identifier
- uon:35259
- Identifier
- ISBN:9783319071237
- Language
- eng
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