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

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

IEEE Transactions on Evolutionary Computation information for authors

Comments Off on IEEE Transactions on Evolutionary Computation information for authors

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.

Comments Off on An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems

Member Get-A-Member (MGM) Program

Comments Off on Member Get-A-Member (MGM) Program

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.

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

Introducing IEEE Collabratec

Comments Off on Introducing IEEE Collabratec

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 that can be used to solve complex tasks. Transfer learning has been introduced to GP to solve complex Boolean and symbolic regression problems with some promise. However, the use of transfer learning with GP has not been investigated to address complex image classification tasks with noise and rotations, where GP cannot achieve satisfactory performance, but GP with transfer learning may improve the performance. In this paper, we propose a novel approach based on transfer learning and GP to solve complex image classification problems by extracting and reusing blocks of knowledge/information, which are automatically discovered from similar as well as different image classification tasks during the evolutionary process. The proposed approach is evaluated on three texture data sets and three office data sets of image classification benchmarks, and achieves better classification performance than the state-of-the-art image classification algorithm. Further analysis on the evolved solutions/trees shows that the proposed approach with transfer learning can successfully discover and reuse knowledge/information extracted from similar or different problems to improve its performance on complex image classification problems.

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

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 verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

Comments Off on Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

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 decomposition multiobjective optimization. In particular, the one-one stable matching between subproblems and solutions, which achieves an equilibrium between their mutual preferences, is claimed to strike a balance between convergence and diversity. However, the original stable marriage model has a high risk of matching a solution with an unfavorable subproblem, which finally leads to an imbalanced selection result. In this paper, we introduce the concept of incomplete preference lists into the stable matching model to remedy the loss of population diversity. In particular, each solution is only allowed to maintain a partial preference list consisting of its favorite subproblems. We implement two versions of stable matching-based selection mechanisms with incomplete preference lists: one achieves a two-level one-one matching and the other obtains a many-one matching. Furthermore, an adaptive mechanism is developed to automatically set the length of the incomplete preference list for each solution according to its local competitiveness. The effectiveness and competitiveness of our proposed methods are validated and compared with several state-of-the-art EMO algorithms on 62 benchmark problems.

Comments Off on Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization

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 solution (i.e., its runtime) is bounded by a polynomial ${Q(n)}$ in the problem size ${n}$ with a probability growing exponentially to 1 with ${n}$ if the parameters of the algorithm are adapted to ${n}$ in a reasonable way. Our polynomial bound ${Q(n)}$ for OneMax outperforms the well-known runtime bound of the 1-ANT algorithm, a particular ant colony optimization algorithm. Our adaptation of the parameters of the CE algorithm balances the number of iterations needed and the size of the samples drawn in each iteration, resulting in an increased efficiency. For the LeadingOnes problem, we improve the runtime of the algorithm by bounding the sampling probabilities away from 0 and 1. The resulting runtime outperforms the known stochastic runtime for a univariate marginal distribution algorithm, and is very close to the known expected runtime of variants of max-min ant systems. Bounding the sampling probabilities allows the CE algorithm to explore the search space even for test functions with a very rugged landscape as the LeadingOnes function.

Comments Off on Stochastic Runtime Analysis of the Cross-Entropy Algorithm