Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology

By J. Holland. 1975

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AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020, background): “(1994), who optimize a local learning rule for a 4-neuron neural network using genetic programming (Holland, 1975; Forsyth et al., 1981; Koza & Koza, 1992).”
Moth–flame optimization algorithm: variants and applications (2019, background): “For example, it provides very quick convergence at a very initial stage by switching from exploration to exploitation, which leads to Holland [54] Kennedy [61] Geem et al. [44] Glover [46] Parameter Three [31] Three [107] Three [85] Five [88] Three [25] Four [135] Complexity O ( nlogn ) [71] O ð n :”
Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm (2016, background): “Several well-known, nature-inspired EAs are: Genetic Algorithm (GA), which works based on the principle of the Darwinian theory of survival [6,7], Particle Swarm Optimization (PSO) [8–11], which works based on the foraging behavior of a swarm of birds [7], Differential Evolution (DE) [12], which is ”
An updated survey of GA-based multiobjective optimization techniques (2000, background): “…importance of each objective. only because of the multiobjective na-The fuzziness of this area lies in the fact ture of most real-world problems but that there is no accepted definition of also because there are still many open optimum as in single-objective optimiquestions in this area.”
Forming Neural Networks Through Efficient and Adaptive Coevolution (1997, methodology): “Since SANE operates on bit strings and uses both mutation and recombination, its search strategy fall under the genetic algorithm Holland, 1975; Goldberg, 1989 method of evolutionary computation.”
Evolutionary Function Approximation for Reinforcement Learning (2006, background): “In fact, in one of the earliest works on evolutionary computation, Holland (Holland, 1975) argues that the reproduction mechanism encourages exploration, since crossover and mutation result in novel genomes, but also encourages exploitation, since each new generation is based on the fittest members ”
Red deer algorithm (RDA): a new nature-inspired meta-heuristic (2020, background): “In 1975, John Holland has developed the genetic algorithm (GA) to solve huge and complex problems, for the ﬁrst time (Holland 1975).”
A Framework for Optimal Planning in Large Distribution Networks (2009, methodology): “As a general rule, any system involving binary relationships can be represented in the form of a graph [9].”
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems (2022, background): “On the other hand, EAs are a type of stochastic global optimization methods nspired by natural evolution and genetic mechanisms [11], such as Genetic Algorithm (GAs) [21], Evolution trategy (ES) [46], Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [12], and History-based daptive Differe”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “In fact, evolutionary algorithms often ﬁnd non-intuitive solutions to complex problems, especially in the context of multiple over-lapping or conﬂicting objectives (Holland, 1992; B € ack, 1996; Ei-ben and Smith, 2015).”
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm (2019, methodology): “These algorithms are: TLBO [137], GWO [80], SFS [18], AMO [78], DE [11], BBO [51], ACO [21], ES [30], GA [6], PSO [24].”
Heuristics for cardinality constrained portfolio optimisation (2000, background): “The theoretical foundations of GAs were originally developed by Holland [24].”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, background): “Many books and articles about this methodology are available, e.g., Mitchell (1998), Holland (2001), or Back (1996).”
Adaptive computation and machine learning (1998, background): “In the present context, evolutionary algorithms [261, 476] perhaps have a special flavor since their source of inspiration, evolution, is at the heart of our domain.”
Where do transactions come from? Modularity, transactions, and the boundaries of firms (2007, methodology): “Building on the theory of Holland (1992) of evolution in complex systems, they then constructed a theory of design evolution via “modular operators.””
Evolving Neural Networks through Augmenting Topologies (2002, background): “The original implicit version of fitness sharing introduced by Holland (1975) grouped individuals by performance similarity rather than genetic similarity.”