Evolutionary computation: Toward a new philosophy of machine intelligence

By Thomas Bäck. 1997

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “This idea led to the belief that intelligent computer programs could emerge with little human intervention by means of evolution in-silico (Holland and Reitman, 1977; Koza, 1992; Fogel, 2006).”
Multiobjective Optimization Using Ideas from the Clonal Selection Principle (2003, methodology): “ESs [36], [37] and evolutionary programming techniques [40], [41] also employ self-adapting parameters, but based on a mathematical framework that is distinct from the biologically inspired mechanism used by CLONALG.”
Evolutionary Algorithms for Solving Multi-Objective Problems (2002, unknown): “Common between them are the reproduction, random variation, competition, and selection of contending individuals within some population (Fogel, 1995).”
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem (2000, background): “1983), evolutionary computation (Holland 1975; Fogel 1994), and tabu search (Glover 1989a,b).”
Evolution-Guided Policy Gradient in Reinforcement Learning (2018, background): “Evolutionary algorithms (EAs) are a class of search algorithms with three primary operators: new solution generation, solution alteration, and selection [21, 52].”
Evolving artificial neural networks (1999, background): “Angeline et al. [149] and Fogel [12], [243] have provided a more general discussion on the mapping between genotypes and phenotypes.”
On the deleterious effects of a priori objectives on evolution and representation (2011, background): “In evolutionary computation (EC), it is traditional to evolve the population iteratively by evaluating its members with respect to the objective [2,4,7,8].”
Evolutionary computation: a unified approach (2018, background): “The interested reader can find additional details about EP in Fogel (1995).”
A fuzzy-genetic approach to breast cancer diagnosis (1999, unknown): “Usually grouped under the term e6olutionary algorithms or e6olutionary computation, we find the domains of genetic algorithms, evolution strategies, evolutionary programming, and genetic programming [6,15,23].”
A framework for evolutionary optimization with approximate fitness functions (2002, background): “However, unlike in the canonical genetic algorithm (GA) [1], but similar to evolutionary programming (EP) [38], the mutation operator obtains a direction during search due”
Phylogeny, Ontogeny, and Epigenesis: Three Sources of Biological Inspiration for Softening Hardware (1996, background): “Usually grouped under the term evolutionary algorithms or evolutionary computation, we find the domains of genetic algorithms, evolution strategies, evolutionary programming, and genetic programming [2, 17, 22, 28, 32, 41, 42, 56].”
Evolutionary ensembles with negative correlation learning (2000, methodology): “In EENCL, an evolutionary algorithm based on evolutionary programming [18] has been used to search for a population of diverse individual”
Particle swarm optimization for integer programming (2002, background): “It di ers from other well{known Evolutionary Algorithms (EA) [2], [4], [7], [14], [30].”
Evolutionary programming using mutations based on the Levy probability distribution (2004, methodology): “Even though EP first used finite-state machines as the underlying structure to be evolved [2], it was extended in the mid-1980s to handle essentially arbitrary data structures [1], such as applications involving continuous parameters and combinatorial optimization problems.”
A new evolutionary system for evolving artificial neural networks (1997, background): “EP [1], [2], [3] is better suited for evolving haviors due to its emphasis on maintaining behavioral links between a parent and its offspring.”