Adaptation in Natural and Artificial Systems¶
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Adaptation
- Behavioral flexibility within a fixed repertoire is not adaptation; evolving the repertoire itself is. Timescale relation. The adaptation timescale is commensurate with the environmental dynamics: fast enough to track meaningful change, slow enough to integrate signal rather than noise.
This sourceFoundational text on genetic algorithms: formalizes the variation–selection–replication cycle as a substrate-independent mechanism for adaptive search, including the calibration of search/exploration rate, in both biology and computation.
- Behavioral flexibility within a fixed repertoire is not adaptation; evolving the repertoire itself is. Timescale relation. The adaptation timescale is commensurate with the environmental dynamics: fast enough to track meaningful change, slow enough to integrate signal rather than noise.
- Natural Selection
- In computer science, evolutionary computation makes the engine an explicit algorithm: genetic algorithms and genetic programming maintain a population of candidate solutions, vary them by mutation and crossover, select differentially according to a fitness function, and retain the fitter candidates to seed the next generation — the variation–selection–retention loop run deliberately to optimize designs, schedules, and programs that no closed-form method could reach.
This sourceIntroduces the genetic algorithm, making variation-selection-retention an explicit optimization procedure over a population of encoded candidate solutions under a fitness function.
- In computer science, evolutionary computation makes the engine an explicit algorithm: genetic algorithms and genetic programming maintain a population of candidate solutions, vary them by mutation and crossover, select differentially according to a fitness function, and retain the fitter candidates to seed the next generation — the variation–selection–retention loop run deliberately to optimize designs, schedules, and programs that no closed-form method could reach.
- Optimization Landscape
- The fitness-landscape framing from evolutionary biology seeded genetic algorithms in computer science, which then transferred into neural architecture search, the landscape vocabulary travelling with the algorithm family.
This sourceSeeds genetic algorithms from the evolutionary fitness-landscape framing. (1975 University of Michigan Press first edition predates DOIs; the 1992 MIT Press reissue carries DOI 10.7551/mitpress/1090.001.0001 but is a different edition.)
- The fitness-landscape framing from evolutionary biology seeded genetic algorithms in computer science, which then transferred into neural architecture search, the landscape vocabulary travelling with the algorithm family.
- Variation Strategies
- They separate stable exploitation (running a known good process) from deliberate perturbation (injecting alternatives to test), and name the mechanisms by which perturbation generates useful signal—a pattern Holland (1975) formalized as the variation-selection-replication cycle underlying genetic adaptation in both natural and artificial systems.
This sourceFoundational text on genetic algorithms: formalizes the variation–selection–replication cycle as a substrate-independent mechanism for adaptive search in both biology and computation.
- They separate stable exploitation (running a known good process) from deliberate perturbation (injecting alternatives to test), and name the mechanisms by which perturbation generates useful signal—a pattern Holland (1975) formalized as the variation-selection-replication cycle underlying genetic adaptation in both natural and artificial systems.
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