Macroevolutionary algorithms: a new optimization method on fitness landscapes
Introduces an approach to optimization problems based on a previous theoretical work on extinction patterns in macroevolution. We name them macroevolutionary algorithms (MA). Unlike population-level evolution, which is employed in standard evolutionary algorithms, evolution at the level of higher ta...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 1999-11, Vol.3 (4), p.272-286 |
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| container_end_page | 286 |
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| container_title | IEEE transactions on evolutionary computation |
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| creator | Marin, J. Sole, R.V. |
| description | Introduces an approach to optimization problems based on a previous theoretical work on extinction patterns in macroevolution. We name them macroevolutionary algorithms (MA). Unlike population-level evolution, which is employed in standard evolutionary algorithms, evolution at the level of higher taxa is used as the underlying metaphor. The model exploits the presence of links between "species" that represent candidate solutions to the optimization problem. To test its effectiveness, we compare the performance of MAs versus genetic algorithms (GA) with tournament selection. The method is shown to be a good alternative to standard GAs, showing a fast monotonous search over the solution space even for very small population sizes. A mean field theoretical approach is presented showing that the basic dynamics of MAs are close to an ecological model of multispecies competition. |
| doi_str_mv | 10.1109/4235.797970 |
| format | Article |
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We name them macroevolutionary algorithms (MA). Unlike population-level evolution, which is employed in standard evolutionary algorithms, evolution at the level of higher taxa is used as the underlying metaphor. The model exploits the presence of links between "species" that represent candidate solutions to the optimization problem. To test its effectiveness, we compare the performance of MAs versus genetic algorithms (GA) with tournament selection. The method is shown to be a good alternative to standard GAs, showing a fast monotonous search over the solution space even for very small population sizes. A mean field theoretical approach is presented showing that the basic dynamics of MAs are close to an ecological model of multispecies competition.</description><identifier>ISSN: 1089-778X</identifier><identifier>EISSN: 1941-0026</identifier><identifier>DOI: 10.1109/4235.797970</identifier><identifier>CODEN: ITEVF5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Biological and medical sciences ; Biological evolution ; Biological system modeling ; Biology computing ; Convergence ; Evolution ; Evolution (biology) ; Evolutionary algorithms ; Evolutionary computation ; Exact sciences and technology ; Flows in networks. Combinatorial problems ; Fundamental and applied biological sciences. Psychology ; Genetic algorithms ; Genetics of eukaryotes. Biological and molecular evolution ; Iron ; Mathematical models ; Operational research and scientific management ; Operational research. 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We name them macroevolutionary algorithms (MA). Unlike population-level evolution, which is employed in standard evolutionary algorithms, evolution at the level of higher taxa is used as the underlying metaphor. The model exploits the presence of links between "species" that represent candidate solutions to the optimization problem. To test its effectiveness, we compare the performance of MAs versus genetic algorithms (GA) with tournament selection. The method is shown to be a good alternative to standard GAs, showing a fast monotonous search over the solution space even for very small population sizes. A mean field theoretical approach is presented showing that the basic dynamics of MAs are close to an ecological model of multispecies competition.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Biological and medical sciences</subject><subject>Biological evolution</subject><subject>Biological system modeling</subject><subject>Biology computing</subject><subject>Convergence</subject><subject>Evolution</subject><subject>Evolution (biology)</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Exact sciences and technology</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Genetic algorithms</subject><subject>Genetics of eukaryotes. 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Combinatorial problems</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Genetic algorithms</topic><topic>Genetics of eukaryotes. Biological and molecular evolution</topic><topic>Iron</topic><topic>Mathematical models</topic><topic>Operational research and scientific management</topic><topic>Operational research. 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| ispartof | IEEE transactions on evolutionary computation, 1999-11, Vol.3 (4), p.272-286 |
| issn | 1089-778X 1941-0026 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Biological and medical sciences Biological evolution Biological system modeling Biology computing Convergence Evolution Evolution (biology) Evolutionary algorithms Evolutionary computation Exact sciences and technology Flows in networks. Combinatorial problems Fundamental and applied biological sciences. Psychology Genetic algorithms Genetics of eukaryotes. Biological and molecular evolution Iron Mathematical models Operational research and scientific management Operational research. Management science Optimization Optimization methods Physics Searching Testing |
| title | Macroevolutionary algorithms: a new optimization method on fitness landscapes |
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