new members to the GPEM editorial board

 GPEM welcomes the following new members to the editorial board: 
 Anna I Esparcia Alcazar,
 Muhammad Atif Azad,
 Mauro Castelli,
 Ting Hu,
 Michael Lones,
 Evelyne Lutton,
 James Mcdermott,
 Xuan Hoai Nguyen,
 Gabriela Ochoa,
 Gisele Pappa,
 Justyna Petke,
 Leonardo Trujillo Reyes,
 Federica Sarro,
 and Alberto Tonda.

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CFP: IEEE CEC 2017 Special Session: Genetics-Based Machine Learning to Evolutionary Machine Learning

Dear Colleagues,


We would like to invite you to submit a paper for the Special Session on Genetics-Based Machine Learning to Evolutionary Machine Learning at 2017 IEEE Congress on Evolutionary Computation (CEC 2017), which will be held in Donostia – San Sebastián, Spain,  June 5-8, 2017. If you are interested in our special session and planning to submit a paper, please let us know beforehand. We would like to have a list of tentative papers. Of course, you can submit it without the reply to this message.


Special Session:  Genetics-Based Machine Learning to Evolutionary Machine Learning

Organizers: Masaya Nakata, Yusuke Nojima, Will Browne, Keiki Takadama, Tim Kovacs


Evolutionary Machine Learning (EML) explores technologies that integrate machine learning with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to a local search while evolutionary computation contributes to a global search, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone.


Historically, this research area was called genetics-based machine learning (GBML) and it was concerned with learning classifier systems (LCS) with its numerous implementations such as fuzzy learning classifier systems (Fuzzy LCS). More recently, EML has emerged as a more general field than GBML; EML covers a wider range of machine learning adapted methods such as genetic programming for ML, evolving ensembles, evolving neural networks, and genetic fuzzy systems; in short, any combination of evolution and machine learning. EML is consequently a broader, more flexible and more capable paradigm than GBML.


From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects. This special session is the third edition of our previous special sessions in CEC2015 and CEC2016. The continuous exploration of this field by organizing the special session in CEC is indispensable to establish the discipline of EML.

– Evolutionary learning systems (e.g., learning classifier systems)

– Evolutionary fuzzy systems

– Evolutionary data mining

– Evolutionary reinforcement learning

– Evolutionary neural networks

– Evolutionary adaptive systems

– Artificial immune systems

– Genetic programming applied to machine learning

– Evolutionary feature selection and construction for machine learning

– Transfer learning; learning blocks of knowledge (memes, code, etc.) and evolving the sharing to related problem domains

– Accuracy-Interpretability trade-off in EML

– Applications and theory of EML


Important dates are as follows:

– Paper Submission Deadline: January 16, 2017

– Paper Acceptance Notification: February 26, 2017

– Final Paper Submission Deadline: TBD

– Conference Dates: June 5-8, 2017


Further information about the special session and the conference can be found at:

– 2017 IEEE Congress on Evolutionary Computation

– Special Session on


Best regards,

Masaya, Yusuke, Will, Keiki, Tim

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Editorial for the Special Issue on Combinatorial Optimization Problems

Evolutionary Computation, Volume 24, Issue 4, Page 573-575, Winter 2016.

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IEEE Transactions on Evolutionary Computation information for authors

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

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Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators

Traditional multiobjective evolutionary algorithms face a great challenge when dealing with many objectives. This is due to a high proportion of nondominated solutions in the population and low selection pressure toward the Pareto front. In order to tackle this issue, a series of indicator-based algorithms have been proposed to guide the search process toward the Pareto front. However, a single indicator might be biased and lead the population to converge to a subregion of the Pareto front. In this paper, a multi-indicator-based algorithm is proposed for many-objective optimization problems. The proposed algorithm, namely stochastic ranking-based multi-indicator Algorithm (SRA), adopts the stochastic ranking technique to balance the search biases of different indicators. Empirical studies on a large number (39 in total) of problem instances from two well-defined benchmark sets with 5, 10, and 15 objectives demonstrate that SRA performs well in terms of inverted generational distance and hypervolume metrics when compared with state-of-the-art algorithms. Empirical studies also reveal that, in the case a problem requires the algorithm to have strong convergence ability, the performance of SRA can be further improved by incorporating a direction-based archive to store well-converged solutions and maintain diversity.

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Member Get-A-Member (MGM) Program

Advertisement, IEEE.

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Memetic Search for the Generalized Quadratic Multiple Knapsack Problem

The generalized quadratic multiple knapsack problem (GQMKP) extends the classical quadratic multiple knapsack problem with setups and knapsack preference of the items. The GQMKP can accommodate a number of real-life applications and is computationally difficult. In this paper, we demonstrate the interest of the memetic search approach for approximating the GQMKP by presenting a highly effective memetic algorithm (denoted by MAGQMK). The algorithm combines a backbone-based crossover operator (to generate offspring solutions) and a multineighborhood simulated annealing procedure (to find high quality local optima). To prevent premature convergence of the search, MAGQMK employs a quality-and-distance (QD) pool updating strategy. Extensive experiments on two sets of 96 benchmarks show a remarkable performance of the proposed approach. In particular, it discovers improved best solutions in 53 and matches the best known solutions for 39 other cases. A case study on a pseudo real-life problem demonstrates the efficacy of the proposed approach in practical situations. Additional analyses show the important contribution of the novel general-exchange neighborhood, the backbone-based crossover operator as well as the QD pool updating rule to the performance of the proposed algorithm.

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2017: Congress on Evolutionary Computation

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.

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An Effective Hybrid Memetic Algorithm for the Minimum Weight Dominating Set Problem

The minimum weight-dominating set (MWDS) problem is NP-hard and has a lot of applications in the real world. Several metaheuristic methods have been developed for solving the problem effectively, but suffering from high CPU time on large-scale instances. In this paper, we design an effective hybrid memetic algorithm (HMA) for the MWDS problem. First, the MWDS problem is formulated as a constrained 0–1 programming problem and is converted to an equivalent unconstrained 0–1 problem using an adaptive penalty function. Then, we develop a memetic algorithm for the resulting problem, which contains a greedy randomized adaptive construction procedure, a tabu local search procedure, a crossover operator, a population-updating method, and a path-relinking procedure. These strategies make a good tradeoff between intensification and diversification. A number of experiments were carried out on three types of instances from the literature. Compared with existing algorithms, HMA is able to find high-quality solutions in much less CPU time. Specifically, HMA is at least six times faster than existing algorithms on the tested instances. With increasing instance size, the CPU time required by HMA increases much more slowly than required by existing algorithms.

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Minimum Manhattan Distance Approach to Multiple Criteria Decision Making in Multiobjective Optimization Problems

A minimum Manhattan distance (MMD) approach to multiple criteria decision making in multiobjective optimization problems (MOPs) is proposed. The approach selects the final solution corresponding with a vector that has the MMD from a normalized ideal vector. This procedure is equivalent to the knee selection described by a divide and conquer approach that involves iterations of pairwise comparisons. Being able to systematically assign weighting coefficients to multiple criteria, the MMD approach is equivalent to a weighted-sum (WS) approach. Because of the equivalence, the MMD approach possesses rich geometric interpretations that are considered essential in the field of evolutionary computation. The MMD approach is elegant because all evaluations can be performed by efficient matrix calculations without iterations of comparisons. While the WS approach may encounter an indeterminate situation in which a few solutions yield almost the same WS, the MMD approach is able to determine the final solution discriminately. Since existing multiobjective evolutionary algorithms aim for a posteriori decision making, i.e., determining the final solution after a set of Pareto optimal solutions is available, the proposed MMD approach can be combined with them to form a powerful solution method of solving MOPs. Furthermore, the approach enables scalable definitions of the knee and knee solutions.

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