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GPEM 18(3) is available, with a peer commentary special section

The third issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download. Along with two regular articles and a book review, it contains a peer commentary special section, with a target article by Peter A. Whigham, Grant Dick, and James Maclaurin, seven commentaries, and a response by the target article authors. The special section is also available as a “topical collection” with its own page here.

GPEM 18(3) contains:

A univariate marginal distribution algorithm based on extreme elitism and its application to the robotic inverse displacement problem
by Shujun Gao & Clarence W. de Silva

A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks
by Ali Mohammad Saghiri & Mohammad Reza Meybodi

Introduction to the peer commentary special section on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Lee Spector

On the mapping of genotype to phenotype in evolutionary algorithms
by Peter A. Whigham, Grant Dick & James Maclaurin

Probing the axioms of evolutionary algorithm design: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Lee Altenberg

Genotype–phenotype mapping implications for genetic programming representation: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Anikó Ekárt & Peter R. Lewis

Evolutionary algorithms and synthetic biology for directed evolution: commentary on “on the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Douglas B. Kell

Distilling the salient features of natural systems: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Whigham, Dick and Maclaurin
by Michael O’Neill & Miguel Nicolau

A rebuttal to Whigham, Dick, and Maclaurin by one of the inventors of Grammatical Evolution: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Conor Ryan

(Over-)Realism in evolutionary computation: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by G. Squillero & A. Tonda

Taking “biology” just seriously enough: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by James A. Foster

Just because it works: a response to comments on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms”
by Peter A. Whigham, Grant Dick & James Maclaurin

BOOK REVIEW
Sebastian Ventura and Jose Maria Luna: Pattern mining with evolutionary algorithms
by Bing Xue

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IEEE Transactions on Evolutionary Computation information for authors

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An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems

In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for developing intelligent agents capable of adapting to the dynamic environment of multiagent systems (MASs). Specifically, we take inspiration from Darwin’s theory of natural selection and Universal Darwinism as the principal driving forces that govern the evolutionary knowledge transfer process. The essential backbone of our proposed eTL comprises several meme-inspired evolutionary mechanisms, namely meme representation, meme expression, meme assimilation, meme internal evolution, and meme external evolution. Our proposed approach constructs social selection mechanisms that are modeled after the principles of human learning to identify appropriate interacting partners. eTL also models the intrinsic parallelism of natural evolution and errors that are introduced due to the physiological limits of the agents’ ability to perceive differences, so as to generate “growth” and “variation” of knowledge that agents have of the world, thus exhibiting higher adaptivity capabilities on solving complex problems. To verify the efficacy of the proposed paradigm, comprehensive investigations of the proposed eTL against existing state-of-the-art TL methods in MAS, are conducted on the “minefield navigation tasks” platform and the “Unreal Tournament 2004” first person shooter computer game, in which homogeneous and heterogeneous learning machines are considered. Continue reading

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Member Get-A-Member (MGM) Program

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Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application

Interactive evolutionary algorithms have been applied to personalized search, in which less user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive estimation of distribution algorithm (IEDA) by using the domain knowledge of personalized search. We first induce a Bayesian model to describe the distribution of the new user’s preference on the variables from the social knowledge of personalized search. Then we employ the model to enhance the performance of IEDA in two aspects, that is: 1) dramatically reducing the initial huge space to a preferred subspace and 2) generating the individuals of estimation of distribution algorithm(EDA) by using it as a probabilistic model. The Bayesian model is updated along with the implementation of the EDA. To effectively evaluate individuals, we further present a method to quantitatively express the preference of the user based on the human-computer interactions and train a radial basis function neural network as the fitness surrogate. The proposed algorithm is applied to a laptop search, and its superiorities in alleviating user fatigue and speeding up the search procedure are empirically demonstrated. Continue reading

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Introducing IEEE Collabratec

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Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimization, image analysis, and classification. Transfer learning is a type of machine learning approach … Continue reading

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Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been ver… Continue reading

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Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization

The balance between convergence and diversity is the cornerstone of evolutionary multiobjective optimization (EMO). The recently proposed stable matching-based selection provides a new perspective to handle this balance under the framework of decomposi… Continue reading

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Stochastic Runtime Analysis of the Cross-Entropy Algorithm

This paper analyzes the stochastic runtime of the cross-entropy (CE) algorithm for the well-studied standard problems OneMax and LeadingOnes. We prove that the total number of solutions the algorithm needs to evaluate before reaching the optimal soluti… Continue reading

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