IEEE Trans Evol Comput

Syndicate content
TOC Alert for Publication# 4235
Updated: 1 year 50 weeks ago

Table of contents

Tue, 12/01/2015 - 00:00
Presents the table of contents for this issue of the publication.

IEEE Transactions on Evolutionary Computation publication information

Tue, 12/01/2015 - 00:00
Provides a listing of the editors, board members, and current staff for this issue of the publication.

A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization

Tue, 12/01/2015 - 00:00
Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.

Switch Analysis for Running Time Analysis of Evolutionary Algorithms

Tue, 12/01/2015 - 00:00
Evolutionary algorithms (EAs) are a large family of heuristic optimization algorithms. They are problem independent and have been applied in various optimization problems. Thus, general analysis tools are especially appealing for guiding the analysis of EAs in various situations. This paper develops the switch analysis approach for running time analysis of EAs, revealing their average computational complexity. Unlike previous analysis approaches that analyze an algorithm from scratch, the switch analysis makes use of another well-analyzed algorithm and, by contrasting them, can lead to better results. We investigate the power of switch analysis by comparing it with two commonly used analysis approaches, the fitness level method and the drift analysis. We define the reducibility between two analysis approaches for comparing their power. By the reducibility relationship, it is revealed that both the fitness level method and the drift analysis are reducible to the switch analysis, as they are equivalent to specific configurations of the switch analysis. We further show that the switch analysis is not reducible to the fitness level method, and compare it with the drift analysis on a concrete analysis case (the discrete linear problem). The reducibility study might shed some light on the unified view of different running time analysis approaches.

A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model

Tue, 12/01/2015 - 00:00
Most researchers employed common functional models when managing scheduling problems with controllable processing times. However, in many complicated manufacturing systems with a high diversity of jobs, these functional resource models fail to reflect their specific characteristics. To fulfill these requirements, we apply a more general model, the discrete model. Traditional functional models can be viewed as special cases of such model. In this paper, the discrete model is implemented on a problem of minimizing the weighted resource allocation subject to a common deadline on a single machine. By reducing the problem to a partition problem, we demonstrate that it is NP-complete, which addresses the difficult issue of the guarantee of both the solution quality and time cost. In order to tackle the problem, we develop an estimation of distribution algorithm based on an approximation of the Boltzmann distribution. The approximation strategy represents a tradeoff between complexity and solution accuracy. The results of the experiments conducted on benchmarks show that, compared with other alternative approaches, the proposed algorithm has competitive behavior, obtaining 74 best solutions out of 90 instances.

An Estimation of Distribution Algorithm With Cheap and Expensive Local Search Methods

Tue, 12/01/2015 - 00:00
In an estimation of distribution algorithm (EDA), global population distribution is modeled by a probabilistic model, from which new trial solutions are sampled, whereas individual location information is not directly and fully exploited. In this paper, we suggest to combine an EDA with cheap and expensive local search (LS) methods for making use of both global statistical information and individual location information. In our approach, part of a new solution is sampled from a modified univariate histogram probabilistic model and the rest is generated by refining a parent solution through a cheap LS method that does not need any function evaluation. When the population has converged, an expensive LS method is applied to improve a promising solution found so far. Controlled experiments have been carried out to investigate the effects of the algorithm components and the control parameters, the scalability on the number of variables, and the running time. The proposed algorithm has been compared with two state-of-the-art algorithms on two test suites of 27 test instances. Experimental results have shown that, for simple test instances, our algorithm can produce better or similar solutions but with faster convergence speed than the compared methods and for some complicated test instances it can find better solutions.

Gene Regulatory Network Evolution Through Augmenting Topologies

Tue, 12/01/2015 - 00:00
Artificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm’s use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments.

A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling

Tue, 12/01/2015 - 00:00
To approximate the Pareto front, most existing multiobjective evolutionary algorithms store the nondominated solutions found so far in the population or in an external archive during the search. Such algorithms often require a high degree of diversity of the stored solutions and only a limited number of solutions can be achieved. By contrast, model-based algorithms can alleviate the requirement on solution diversity and in principle, as many solutions as needed can be generated. This paper proposes a new model-based method for representing and searching nondominated solutions. The main idea is to construct Gaussian process-based inverse models that map all found nondominated solutions from the objective space to the decision space. These inverse models are then used to create offspring by sampling the objective space. To facilitate inverse modeling, the multivariate inverse function is decomposed into a group of univariate functions, where the number of inverse models is reduced using a random grouping technique. Extensive empirical simulations demonstrate that the proposed algorithm exhibits robust search performance on a variety of medium to high dimensional multiobjective optimization test problems. Additional nondominated solutions are generated a posteriori using the constructed models to increase the density of solutions in the preferred regions at a low computational cost.

Evolutionary Nonlinear Projection

Tue, 12/01/2015 - 00:00
This paper examines evolutionary nonlinear projection (NLP), a form of multidimensional scaling (MDS) performed with an evolutionary algorithm. MDS is a family of techniques for producing a low dimensional data set whose points have a one-to-one correspondence with the points of a higher dimensional data set with the added property that distances or dissimilarities in the higher dimensional space are preserved as much as possible in the lower dimensional space. The goal is typically visualization but may also be clustering or other forms of analysis. In this paper, we review current methods of NLP and go on to characterize NLP as an evolutionary computation problem, gaining insight into MDS as an optimization problem. Two different mutation operators, one introduced in this paper, are compared and parameter studies are performed on mutation rate and population size. The new mutation operator is found to be superior. NLP is found to be a problem where small population sizes exhibit superior performance. It is demonstrated experimentally that NLP is a multimodal optimization problem. Two broad classes of projection problems are identified, one of which yields consistent high-quality results and the other of which has many optima, all of low quality. A number of applications of the technique are presented, including projections of feature vectors for polyominos, of vectors that are members of an error correcting code, of behavioral assessments of a collection of agents, and of features derived from DNA sequences.

A Hybrid Swarm-Based Approach to University Timetabling

Tue, 12/01/2015 - 00:00
This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter’s examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

Using Learning Classifier Systems to Learn Stochastic Decision Policies

Tue, 12/01/2015 - 00:00
To solve reinforcement learning problems, many learning classifier systems (LCSs) are designed to learn state-action value functions through a compact set of maximally general and accurate rules. Most of these systems focus primarily on learning deterministic policies by using a greedy action selection strategy. However, in practice, it may be more flexible and desirable to learn stochastic policies, which can be considered as direct extensions of their deterministic counterparts. In this paper, we aim to achieve this goal by extending each rule with a new policy parameter. Meanwhile, a new method for adaptive learning of stochastic action selection strategies based on a policy gradient framework has also been introduced. Using this method, we have developed two new learning systems, one based on a regular gradient learning technology and the other based on a new natural gradient learning method. Both learning systems have been evaluated on three different types of reinforcement learning problems. The promising performance of the two systems clearly shows that LCSs provide a suitable platform for efficient and reliable learning of stochastic policies.

Network Structural Balance Based on Evolutionary Multiobjective Optimization: A Two-Step Approach

Tue, 12/01/2015 - 00:00
Research on network structural balance has been of great concern to scholars from diverse fields. In this paper, a two-step approach is proposed for the first time to address the network structural balance problem. The proposed approach involves evolutionary multiobjective optimization, followed by model selection. In the first step, an improved version of the multiobjective discrete particle swarm optimization framework developed in our previous work is suggested. The suggested framework is then employed to implement network multiresolution clustering. In the second step, a problem-specific model selection strategy is devised to select the best Pareto solution (PS) from the Pareto front produced by the first step. The best PS is then decoded into the corresponding network community structure. Based on the discovered community structure, imbalanced edges are determined. Afterward, imbalanced edges are flipped so as to make the network structurally balanced. Extensive experiments on synthetic and real-world signed networks demonstrate the effectiveness of the proposed approach.

Acknowledgment to Reviewers—2015

Tue, 12/01/2015 - 00:00
Presents a listing of reviewers who contributed to the publication in 2014-2015.

IEEE World Congress on Computational Intelligence

Tue, 12/01/2015 - 00:00
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Special Issue on Evolutionary Many-Objective Optimization

Tue, 12/01/2015 - 00:00
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Introducing IEEE Collabratec

Tue, 12/01/2015 - 00:00
Advertisement, IEEE.

Member Get-A-Member (MGM) Program

Tue, 12/01/2015 - 00:00
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

how can you get your idea to market first

Tue, 12/01/2015 - 00:00
Advertisement, IEEE.

IEEE Computational Intelligence Society Information

Tue, 12/01/2015 - 00:00
Provides a listing of board members, committee members and society officers.

IEEE Transactions on Evolutionary Computation information for authors

Tue, 12/01/2015 - 00:00
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.