Please note that a few new positions will become available for joining the Editorial Board of the IEEE Transactions on Evolutionary Computation (TEVC). Candidates interested in applying must submit via email, in a single PDF file, to the Editor-in-Chief of TEVC (Prof. Carlos A. Coello Coello at [email protected]) the following information:

• A detailed CV in English. It's particularly important that you clearly mark your journal publications related to evolutionary computation. Also, please provide details of your academic/industrial credentials as well as your affiliation, including your current position. It is desirable that you also provide an institutional webpage whenever possible.

• Publications in TEVC (particularly in the last 5 years). Please provide as much detail as possible.

• Special issues at TEVC and/or Special Sessions that you had organized at the IEEE Congress on Evolutionary Computation (CEC). Provide as much detail as possible including the names of your co-organizers/guest co-editors as well as the number of papers submitted and accepted.

• Are you an IEEE and/or CIS member? If so, what is your status (i.e., Member, Senior Member, Fellow)?

• From 4 to 8 areas of expertise within evolutionary computation in which you could handle submissions (please indicate relevant publications from your CV in each of the areas selected). Please note that we are particularly interested in specialists in the following topics: artificial immune systems, theoretical aspects of evolutionary computation, ant colony optimization, parallelism, cryptography, evolvable hardware.

• Prior experience as a reviewer for TEVC. Have you reviewed papers for TEVC? If so, since what year or within what time period? Please provide as much detail as possible.

The committee shall evaluate the relevant values and merits of each nominee using both qualitative and quantitative evidence. The quantitative measures indicated in section III.1 are part of the input to be considered by the committee. Other criteria include, but are not limited to, qualitative assessments of the nominees work, geographical lecturer distribution requirements, or the need to include lecture topics that would not be, otherwise, well represented. The committee may consider exceptions to these criteria on a case by case basis.

Deadline: 30 August 2023

For more information please visit Nominatiing and Appointing DLs.

The IEEE CIS Distinguished Lecturer Program (DLP) aims at serving the Computational Intelligence community by providing stimulating lectures given by distinguished IEEE CIS professionals and scholars. It also aims at helping young researchers (masters and doctoral students) providing suggestions/guidance on their research problems as well as promoting computational intelligence tools and techniques among undergraduate students to make them interested in CIS fields of interest. Distinguished Lecturers present novel basic and/or applied research results in their CIS sub-field. Their lectures offer insights into the trends and challenges of their CIS sub-field and their vision for the given sub-field. In any given year, selected IEEE CIS Distinguished Lecturers (DLs) have a balanced representation of CIS sub-fields and include representatives from academia, industry and government while spanning different IEEE regions. The program supports local CIS Chapters by enabling a major chapter event which can considerably improve a chapter’s visibility to their existing IEEE CIS members, other IEEE society members and the wider CIS community in their area. It is expected that DLP events actively seek to recruit new CIS professionals to both the CIS field and to IEEE CIS.

It should be noted that this program is NOT intended as a means to finance speakers for conferences/workshops or symposia.

Please visit Organizing Distinguished Lectures for more information. 

How Good is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep learning, especially deep reinforcement learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed neural combinatorial optimization (NCO). However, the advantages and disadvantages of NCO relative to other approaches have not been empirically or theoretically well studied. This work presents a comprehensive comparative study of NCO solvers and alternative solvers. Read More

IEEE Computational Intelligence Magazine, August 2023

Federated Fuzzy Neural Network With Evolutionary Rule Learning

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are nonindependent and identically distributed (non-IID). In this article, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Read More

IEEE Transactions on Fuzzy Systems, May 2023

Deep Reinforcement Learning Based Adaptive Operator Selection for Evolutionary Multi-Objective Optimization

Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-objective optimization, where a number of variation operators have been developed to handle the problems with various difficulties. While most EAs use a fixed operator all the time, it is a labor-intensive process to determine the best EA for a new problem. Hence, some recent studies have been dedicated to the adaptive selection of the best operators during the search process. To address the exploration versus exploitation dilemma in operator selection, this paper proposes a novel operator selection method based on reinforcement learning. In the proposed method, the decision variables are regarded as states and the candidate operators are regarded as actions. Read more

IEEE Transactions on Emerging Topics in Computational Intelligence, August 2023

An Iterative Two-Stage Multifidelity Optimization Algorithm for Computationally Expensive Problems

Engineering design optimization often involves use of numerical simulations to assess the performance of candidate designs. The simulations for computing high-fidelity (HF) performance estimates, such as finite element analysis or computational fluid dynamics, are typically computationally expensive. In some cases, it may also be possible to run an alternate or cheaper version of the simulation (through, e.g., use of a coarse mesh) to yield a low-fidelity (LF) performance estimate. Multifidelity optimization refers to the class of methods that aim to manage LF and HF evaluations efficiently to optimize computationally expensive problems within a limited computing budget. Read More

IEEE Transactions on on Evolutionary Computation, June 2023

Learning Abstract Representations Through Lossy Compression of Multimodal Signals

True Random Number Generators (TRNGs) are the best suited for use in cryptography as their outputs are independent and hard to predict. Still, TRNGs often 1) require expensive, and sometimes large, equipment; 2) are mostly non-portable; and 3) are non-reproducible, hence, not considered for computer simulations. On the other hand, PRNGs overcome many of the TRNGs disadvantages, yet finding good mathematical generators is a difficult task. In this respect, deep learning provides several attractive properties, where the mathematical functions and coefficients are learned automatically instead of deriving them by hand. Deep Neural Networks (DNNs) are non-linear mathematical functions whose input cannot be precisely reconstructed from the output, making them attractive as PRNGs. Read More

IEEE Transactions on Cognitive and Developmental Systems, June 2023

• IEEE ToG Special Issue on Human-Centred AI in Game Evaluation (1 October 2023)
  

• IEEE TAI Special Issue on Responsible AI Engineering (1 October 2023)

• IEEE TFS Special Issue on Fuzzy Systems Toward Human-Inspired Computational Intelligence in Complex Systems (31 October 2023)

• IEEE TFS Special Issue on Smart Fuzzy Optimization in Sustainable Manufacturing and Supply Chain Management (1 November 2023)

• IEEE TFS Special Issue on Advances in fuzzy deep learning algorithms for biomedical data (1 December 2023)

• IEEE TETCI Special Issue on Quantum Computing meets Computational Intelligence: A New Vision for Smart Systems (20 December 2023)

• IEEE TFS Special Issue on Deep Neuro-Fuzzy Approaches for Intelligent Big Data Processing (30 December 2023)

• IEEE TETCI Special Issue on Advances in Methodologies for Metaheuristic Algorithms (31 December 2023)

• IEEE TFS Special Issue on Fuzzy-Deep Neural Network Learning in Sentiment Analysis (15 January 2024)

IEEE CIS sponsors and co-sponsors a number of conferences across the globe.