Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Dynamic constrained optimization problems (DCOPs) are difficult to solve because both objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been proposed to simulate the dynamics of multiple disconnected feasible regions. In this paper, first, the idea of tracking multiple feasible regions, originally proposed by Nguyen and Yao, is enhanced by specifically adopting multiple subpopulations. To this end, the dynamic species-based particle swam optimization (DSPSO), a representative multipopulation algorithm, is adopted. Second, an ensemble of locating and tracking feasible regions strategies is proposed to handle different types of dynamics in constraints. Third, two benchmarks are designed to simulate the DCOPs with dynamic constraints. The first benchmark, including two variants of G24 (called G24v and G24w), could control the size of feasible regions. The second benchmark, named moving feasible regions benchmark (MFRB), is highly configurable. The global optimum of MFRB is calculated mathematically for experimental comparisons. Experimental results on G24, G24v, G24w, and MFRB show that the DSPSO with the ensemble of strategies performs significantly better than the original DSPSO and other typical algorithms.

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Member Get-A-Member (MGM) Program

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Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes

This paper introduces necessary and sufficient conditions that surrogate functions must satisfy to properly define frontiers of nondominated solutions in multiobjective optimization (MOO) problems. These new conditions work directly on the objective space, and thus are agnostic about how the solutions are evaluated. Therefore, real objectives or user-designed objectives’ surrogates are allowed, opening the possibility of linking independent objective surrogates. To illustrate the practical consequences of adopting the proposed conditions, we use Gaussian processes (GPs) as surrogates endowed with monotonicity soft constraints and with an adjustable degree of flexibility, and compare them to regular GPs and to a frontier surrogate method in the literature that is the closest to the method proposed in this paper. Results show that the necessary and sufficient conditions proposed here are finely managed by the constrained GP, guiding to high-quality surrogates capable of suitably synthesizing an approximation to the Pareto frontier in challenging instances of MOO, while an existing approach that does not take the theory proposed in consideration defines surrogates which greatly violate the conditions to describe a valid frontier.

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Structured Memetic Automation for Online Human-Like Social Behavior Learning

Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin’s theory of natural selection and Dawkins’ notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents’ mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior.

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IEEE Transactions on Evolutionary Computation publication information

Provides a listing of the editorial board, current staff, committee members and society officers.

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A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization

Taking both convergence and diversity into consideration, this paper suggests a vector angle-based evolutionary algorithm for unconstrained (with box constraints only) many-objective optimization problems. In the proposed algorithm, the maximum-vector-angle-first principle is used in the environmental selection to guarantee the wideness and uniformity of the solution set. With the help of the worse-elimination principle, worse solutions in terms of the convergence (measured by the sum of normalized objectives) are allowed to be conditionally replaced by other individuals. Therefore, the selection pressure toward the Pareto-optimal front is strengthened. The proposed method is compared with other four state-of-the-art many-objective evolutionary algorithms on a number of unconstrained test problems with up to 15 objectives. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in keeping a good balance between convergence and diversity. Furthermore, it was shown by the results on two problems from practice (with irregular Pareto fronts) that our method significantly outperforms its competitors in terms of both the convergence and diversity of the obtained solution sets. Notably, the new algorithm has the following good properties: 1) it is free from a set of supplied reference points or weight vectors; 2) it has less algorithmic parameters; and 3) the time complexity of the algorithm is low. Given both good performance and nice properties, the suggested algorithm could be an alternative tool when handling optimization problems with more than three objectives.

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IEEE Transactions on Evolutionary Computation Society Information

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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

http://www.cec2017.org/#special_session_sessions

– Special Session on http://www.cec2017.org

https://sites.google.com/site/cec2017ssndeml/home

 

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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