Evolutionary Optimization in Dynamic Environments

By J. Branke. Genetic Algorithms and Evolutionary Computation, 2001

864 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f65c449c90566ca874bc85f4cbf2628e65b0ed51

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Citations in the corpus

Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, methodology): “Compared with previously published results on the MPB, namely, SOS [13] and a hierarchical PSO [24], our approaches cut the ofﬂine error approximately by half.”
Multi-swarm Optimization in Dynamic Environments (2004, background): “The primary performance measure is the offline error [7] which is recorded for each experiment and then averaged, for each algorithm, over 50 experiments with different ran- T. Blackwell and J. Branke dom seeds (which also means different instances of the class of benchmark problems ).”
Evolutionary computation: a unified approach (2018, background): “this area, as reflected by the number of workshops and conference papers, and entire PhD theses on the subject such as Branke (2002) or Morrison (2004).”
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach (2006, background): “Two basic categorizations of dynamic environments are the frequency and the severity of change ([11], [12]).”
Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling (2007, background): “Although single-objective dynamic optimization have received some attention in the past [2], the dynamic multi-objective optimization is yet to receive a significant attention.”
Evolutionary dynamic optimization: A survey of the state of the art (2012, background): “According to [18,13,22,21], a good benchmark problem is one that has the following characteristics:”
Evolutionary computation (2011, background): “However, suitable variations do quite well (see, for example 18,19 ).”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, methodology): “So, we propose a new categorization for concept drift, seeking inspiration from the dynamic optimization problems area [27], [28], [29].”
Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization (2018, background): “through dynamic evolutionary optimization; (Branke 2002)).”
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization (2009, background): “Comprehensive discussions on dynamic SO evolutionary algorithms (SOEAs) can be found in [4] and [45].”
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “Using evolutionary algorithms to solve dynamic optimization problems has attracted increasing attention in recent years [20], [23], [24].”
Swarm Intelligence in Optimization (2008, methodology): “also demonstrated that the quantum particle model in [11] can be incorporated into SPSO to improve its optima-tracking performance for the moving peaks problem [24].”
A survey on metaheuristics for stochastic combinatorial optimization (2009, background): “For problems involving time varying information, there are excellent surveys by Morrison (2004) and Branke (2001, 2002).”
Handling multiple objectives with particle swarm optimization (2004, background): “We are also considering the possibility of extending this algorithm so that it can deal with dynamic functions [1].”