Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)

By Thomas Bäck. 2006

1,304 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7882bbb050acba8297225a6fdd54ac727cfa7eb3

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In Artificial Intelligence

Citations in the corpus

A New Adaptive Merging and Growing Algorithm for Designing Artificial Neural Networks (2009, methodology): “In addition, evolutionary approaches, such as genetic algorithms [5], evolutionary programming [6], [7], and evolution strategies [8], have been used extensively in designing ANNs automatically.”
Evolutionary Computation Meets Machine Learning: A Survey (2011, background): “Introduction Evolutionary computation (EC) [1] is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms.”
A survey on optimization metaheuristics (2013, background): “Fogel to solve more general tasks including prediction problems, numerical and combinatorial optimization, and machine learning [91, 92].”
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics (2005, methodology): “Together with GAs and evolutionary programming, the evolution strategies form the class of evolutionary algorithms (Fogel 1995).”
On the computation of all global minimizers through particle swarm optimization (2004, background): “The most frequently encountered population-based EC techniques, such as evolution strategies (ES) [2]–[7], genetic algorithms (GAs) [8], [9], genetic programming [10], [11], and evolutionary programming [12], are inspired from the evolutionary mechanisms of nature.”
A hybrid genetic algorithm and particle swarm optimization for multimodal functions (2008, methodology): “Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO.”
On the mean‐field limit for the consensus‐based optimization (2021, background): “Noble examples of metaheuristics include Simplex Heuristics [27], Evolutionary Programming [12], Genetic Algorithms [20], Particle Swarm Optimization [21], Ant Colony Optimization [10], and Simulated Annealing [1].”
Agent-based Computational Finance (2006, background): “21A nice balanced overview of all these methods is Fogel (1995). the behavior, and also can be easily manipulated by evolutionary operators.”
Optimization for problem classes-neural networks that learn to learn (2000, background): “In most cases the parents of the succeeding generation are chosen us- ing the EP-tournament-selection (Fogel 1995).”
A survey on evolutionary computation for complex continuous optimization (2021, methodology): “Among them, evolutionary computation (EC) (Fogel 1995) has become a good kind of global optimization technique in many optimization problems.”
Novel meta-heuristic bald eagle search optimisation algorithm (2019, methodology): “Evolutionary optimisation techniques are the most widely used intelligent computing techniques to solve many problems, such as the combinatorial and nonlinear optimisation problems (Fiacco and McCormick 1968).”
A particle swarm optimization approach for optimum design of PID controller in AVR system (2004, methodology): “…[i.e., where the parameters being optimized are highly correlated (the crossover and mutation operations cannot ensure better fitness of offspring because chromosomes in the population have similar structures and their average fitness is high toward the end of the evolutionary process)] [10], [14].”
Emerging artificial intelligence methods in structural engineering (2018, neutral): “MI are almost identical terms [2,3] and are often used interchangeably.”
Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, methodology): “The classical steps for evolutionary algorithms have been summarized in [10] and are as follows: (i) a population of candidate solutions is initialized, and each solution is encoded using alphabet of cardinality; (ii) each solution is evaluated with a fitness value; (iii) new solutions are generated”
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, methodology): “For comparison, we also employed an improved gradientbased training algorithm: scaled conjugate gradient (SCG) backpropagation [52]; and four EA-based training algorithms, namely, simple genetic algorithm (SGA) [35] based algorithm; evolutionary programming (EP) [36], [37] based algorithm; evolution”