Evolutionary programming using mutations based on the Levy probability distribution

By Chang-Yong Lee, Xin Yao. IEEE Transactions on Evolutionary Computation, 2004

496 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7462570ddb98a31c66a049f304f4487be8466298

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. Shahryar Rahnamayan
3. Swagatam Das
4. Sanyang Liu
5. Weifeng Gao
6. Yaochu Jin
7. Janez Brest
8. Pinar Çivicioglu

In Computational Theory and Mathematics

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Shahryar Rahnamayan
4. Sanyang Liu
5. Weifeng Gao
6. Yaochu Jin
7. Janez Brest
8. Pinar Çivicioglu

Citations in the corpus

• A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning (2013, methodology): “These algorithms include evolutionary programming with adaptive Levy mutation (ALEP) [41], fast evolutionary programming (FEP) with Cauchy mutation [42], orthogonal GA with quantization (OGA/Q) [36], estimation of distribution algorithm with local search (EDA/L) [43], conventional”
• A survey on optimization metaheuristics (2013, methodology): “In [162], Lee and Yao proposed to use a Levy-distributi on for higher variation s and a greater diversity.”
• Large scale evolutionary optimization using cooperative coevolution (2008, background): “Among the 13 functions, functions f1—f7 are unimodal functions and functions f8—f13 are multimodal functions where the number of local minima increases exponentially as the problem dimension increases [5,27].”
• JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “For the ellipsoid function f 3 , μ F and μ CR go to steady states after obvious initial changes (i.e., the population, which is initially distributed in 1 In this paper, we define f 8 as a shifted version of that in [15], [30], and [40] so that its optimal value is equal to 0.”
• Ensemble of Constraint Handling Techniques (2010, methodology): “EP commences with a randomly initialized population which generates an offspring population by mutation using either Gaussian (classical EP, CEP) [36], [39] or Levy [40] or Cauchy (fast EP, FEP) [41] or a mixed mutation [42] strategy.”
• Opposition-Based Differential Evolution (2008, unknown): “• Population size, [15], [33], [34].”
• Quasi-oppositional Differential Evolution (2007, methodology): “Parameter settings for all conducted experiments are as follows: • Population size, N p = 100 [2], [4], [23] • Differential amplification factor, F = 0 .”
• Population size reduction for the differential evolution algorithm (2008, background): “A more detailed description of all functions is given in [16, 30].”
• Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “Other probability distributions were suggested and showed effective on different fitness landscapes (Yao and Liu, 1999; Lee and Yao, 2004), while in (Soltoggio, 2005) and (Soltoggio, 2006) it was shown how different mutation operators can benefit specific problems.”
• Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, methodology): “In the experiment, we used the same function set and the parameters as in [24].”