The self-adaptive Pareto differential evolution algorithm

By Hussein A. Abbass. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

445 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/d18fccc485135f895b1280208735941d764bb108

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Citations in the corpus

Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “Teo proposed DE with self-adapting populations (DESAP) [27], based on Abbass’s self-adaptive Pareto DE [25].”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, methodology): “Abbass [1] used the typical mutation formula of DE, Brest et al.”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, background): “In addition, several researchers [18], [24]–[26] focused on the adaptation of the control parameters and .”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “In Abbass (2001) and Abbass and Sarker (2002) the concept of Pareto dominance is integrated within a DE for extending the algorithmic functioning to the multi-objective case. In Abbass (2002) an improvement of the algorithm in Abbass (2001) is proposed by including self-adaptation within variation o”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “Similarly, Pareto differential evolution (PDE) algorithm and its variant self-adaptive PDE algorithm was applied to optimize objectives ctrn and cnet simultaneously that offered a Pareto-set, from which the best solution was picked-up according to network complexity and approximation error examinati”
Differential evolution with multi-population based ensemble of mutation strategies (2016, methodology): “15), whereas the scale factor F was adapted analogously to the adaptation of the crossover rate CR in [1].”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “In [S64], Abbass presented an approach called Memetic Pareto artificial neural networks.”
GDE3: the third evolution step of generalized differential evolution (2005, background): “Later on, Self-adaptive PDE (SPDE) [1], the Pareto DE Approach (PDEA) [26], Adaptive Pareto DE (APDE) [37], Multi-Objective DE (MODE) [13], and Vector Evaluated DE (VEDE) [29] have been proposed.”
JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “The SPDE in [ 9 ] for multiobjective optimization (MOO) and the DESAP in [10] belong to this category.”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, background): “In evolutionary multi-objective optimization in general, the use of self-adaptation or on-line adaptation mechanisms is scarce (see for example [63, 1, 7]), and we are only aware of one multi-objective evolutionary algorithm which was designed to be parameterless: the microGA(2) [65].”
Exploring dynamic self-adaptive populations in differential evolution (2006, background): “The crossover rate δ and mutation rate η are assigned random values according to a uniform distribution between [0, 1].”
A Fuzzy Adaptive Differential Evolution Algorithm (2002, methodology): “A self-adaptive approach to DE parameters, the mutation amplification and the crossover parameter, was presented in [1].”
DEMO: Differential Evolution for Multiobjective Optimization (2005, methodology): “Abbass [5, 6] was the first to apply DE to MOPs in the so-called Pareto Differential Evolution (PDE) algorithm.”