A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization

By Kalyanmoy Deb, Ashish Anand, Dhiraj Joshi. Evolutionary Computation, 2002

653 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e4deba81c0c0cf8a0c32064ed59b8216d3412a33

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Pinar Çivicioglu
4. Yaochu Jin
5. Francisco Herrera
6. Seyedali Mirjalili
7. Ajith Abraham
8. Ferrante Neri

In Computational Theory and Mathematics

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Pinar Çivicioglu
4. Yaochu Jin
5. Francisco Herrera
6. Ferrante Neri
7. Ajith Abraham
8. Seyedali Mirjalili

Citations in the corpus

• Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, background): “In this context, four different approaches have found widespread use: i) self-adaptive evolution strategies [3], [19], [34]; ii) real-parameter genetic algorithms [10], [11], [24]; iii) differential evolution methods [37]; and iv) particle swarm optimization (PSO) algorithms [13], [26].”
• Accelerating Differential Evolution Using an Adaptive Local Search (2008, methodology): “We also experimented with other crossover operations like UNDX [3], PCX [45], BLX- [46], parent centric BLX-(PBX-) [17], and we found that the performance of those XLS were influenced by the adaptive capability of the crossover schemes.”
• Self-adaptive simulated binary crossover for real-parameter optimization (2007, background): “Past studies of real-coded genetic algorithms with the SBX operator was not found to be suitable for multi-modal problems, such as Rastrigin’s function [4].”
• Multi-objective optimization using evolutionary algorithms (2001, background): “Besides the variable-wise recombination operators, vector-wise recombination operators also suggested to propagate the correlation among variables of parent solutions to the created child solutions [6, 7].”
• Real-Coded Memetic Algorithms with Crossover Hill-Climbing (2004, methodology): “However, multiparent crossover operators have been proposed which combine the features of more than two parents for generating the offspring ( Deb, Anand and Joshi, 2002; Kita, Ono and Kobayashi, 1999; Tsutsui, Yamamura and Higuchi, 1999;).”
• Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, positive): “DEPCX) [67] are inspired by the parent centric crossover operator (PCX) used in GAs [68].”
• Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization (2008, background): “However, in the recent years, real-parameter GAs are developed to work on real-valued variables using a number of recombination operators (such as SBX [20], PCX [8], UNDX [21], and others) and mutation operators (polynomial mutation, non-uniform mutation, and others [22]).”
• Novel composition test functions for numerical global optimization (2005, methodology): “05 Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) [9]: x0: random position in the search space, expected initial distance to optimum per coordinate Coordist=5 G3 model with PCX crossover (G3-PCX) [10]: population size=150, μ =3, λ =2, replace number=1”
• Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction (2011, background): “Besides the variable-wise recombination operators, vector-wise recombination operators also suggested to propagate the correlation among variables of parent solutions to the created child solutions [6, 7].”
• A Note on the Extended Rosenbrock Function (2006, background): “It is claimed in (Deb, Anand, and Joshi, 2002) that three minima were identified for the 20-variable Rosenbrock function through experiment.”
• Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “[60] proposed a generic parentcentric recombination scheme (PCX) and integrated it with a steady state, elite preserving, scalable, and computationally fast population alteration model of the GA, which they named the G3 (generalized generation gap) model.”
• Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II (2020, background): “the offspring to be created nearer to the parents with high probability [49].”