Advances in Computational Intelligence

By Hans-Paul Schwefel, Ingo Wegener, Klaus Weinert. Natural Computing Series, 2003

1,238 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cf0fa59f44c25cde5addd357db3fd0b63c7aab33

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In Artificial Intelligence

Citations in the corpus

Block-Based Neural Networks for Personalized ECG Signal Classification (2007, methodology): “Two successive populations differ by a single individual in this incremental EA [27], [ 28 ].”
From evolutionary computation to the evolution of things (2015, background): “Evolutionary algorithms have overcome considerable initial scepticism, and over the last twenty years evolutionary computation has grown to become a major field in computational intelligence 7,8,9 .”
The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm (2009, neutral): “Among the problem solving techniques inspired from nature are evolutionary computation (Eiben and Smith, 2003), neural networks (Haykin, 1999), time adaptive self-organising maps (Shah-Hosseini, 2006), ant colony optimisation (Dorigo and Stutzle, 2004), bee colony optimisation (Sato and Hagiwara,…”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, background): “Later on in a joint publication, these views were merged [61] contains a discussion of methods for parameter control in EAs and a critical view on existing taxonomies therein.”
Autostacker: a compositional evolutionary learning system (2018, background): “• Evolutionary Search Algorithm EAs allow us to tractably find good solutions in a large space of variables [9].”
Global Optimization Algorithms -- Theory and Application (2009, neutral): “Fogel [453]: Evolutionary Computation: Principles and Practice for Signal Processing Ashlock [454]: Evolutionary Computation for Modeling and Optimization Watanabe and Hashem [340]: Evolutionary Computations – New Algorithms and their Applications to Evolutionary Robots Cagnoni, Lutton, and Olague [”
Robots that can adapt like animals (2014, methodology): “More details and experiments about MAP-Elites are available in (Mouret and Clune, 2015) 31 .”
ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, positive): “We have had success with a genetic algorithm (Eiben and Smith 2003), which introduces random variation (‘‘mutations’’) each iteration into parameter values and selects new parameters based on the height of the likelihood (‘‘fitness’’).”
Choosing Mutation and Crossover Ratios for Genetic Algorithms - A Review with a New Dynamic Approach (2019, background): “The researcher started with problem encoding (representation of the problem) as a first step to translate the problem to computer language [21].”
Metaheuristics (2016, background): “Thus, in problems of placement of components, the tasks can be naturally distributed between several processors: each one of them is responsible for optimizing a given geographical area and information is exchanged periodically between nearby processors (see, for example, [20, 22]).”
Ant colony optimization theory: A survey (2005, neutral): “It would be interesting to obtain results on convergence speed for ACO algorithms , in spirit similar to what has been done in evolutionary computation for relatively simple problems such as , for example , ONE-MAX [43].”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, methodology): “Moreover, according to Table IX, the average fitness evaluations used by DEahcSPX were fewer than that used by MGG UNDX to achieve the target accuracy levels .”
Electric load forecasting methods: Tools for decision making (2009, methodology): “In Huo et al. (2007) genetic programming (see e.g. Eiben and Smith, 2003) was used directly for load forecasting.”
On the deleterious effects of a priori objectives on evolution and representation (2011, background): “a cycle of evaluation, selection, and mutation is applied repeatedly to shape a population with respect to an objective [2,4].”