Artificial Intelligence through Simulated Evolution

By L. Fogel, A. J. Owens, M. J. Walsh. 1966

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Citations in the corpus

On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1, except that C is now an FNN or RNN trained by evolutionary algorithms [200, 255, 105, 56, 68] applied to NNs [165, 321, 180, 259, 72, 90, 89, 110, 94], or by policy gradient methods [314, 315, 316, 274, 18, 1, 63, 128, 313, 210, 192, 191, 256, 85, 312, 190, 82, 93][245, Sec.”
Evolutionary programming techniques for economic load dispatch (2003, methodology): “Roughly 40 years ago, EP was proposed for the evolution of the finite-state machines in order to solve prediction tasks [ 20 ].”
Evolutionary dynamic optimization: A survey of the state of the art (2012, background): “Although a few EDO works appeared in the early days of EC [16,17], the field is still relatively young since most of the studies on EDO have been made in the last 20 years.”
A Hybrid LR-EP for Solving New Profit-Based UC Problem under Competitive Environment (2002, background): “In order to overcome these problems, many stochastic optimizations such as genetic algorithm (GA) and evolutionary programming [7]‐[ 12 ] were introduced into power system optimization.”
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem (2000, unknown): “programming [66], evolution strategies[67], genetic algo-”
Symbiotic Evolution of Neural Networks in Sequential Decision Tasks (1997, methodology): “Methods from genetic algorithms (Holland 1975; Goldberg 1989), evolutionary programming (Fogel et al. 1966), genetic programming (Koza 1992), or evolutionary strategies (Rechenberg 1964) could all be used in this framework to form eeective decision-making agents.”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, background): “3 Historically, three major EAs have been developed: Evolution Strategies (ES) [11,12], Evolutionary Programming (EP) [13], and Genetic Algorithms (GAs) [9].”
A comparative study of Artificial Bee Colony algorithm (2009, background): “Although Genetic Algorithm (GA) [3], Genetic Programming (GP) [4], Evolution Strategy (ES) [5,6] and Evolutionary Programming (EP) [7] are popular evolutionary algorithms, GA is the most widely used one in the literature.”
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, methodology): “The well-known algorithms in the individual-based family are: Tabu Search (TS) [6, 9], hill climbing [10] , Iterated Local Search (ILS) [11], and Simulated Annealing (SA) [12].”