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Discrete Planar Truss Optimization by Node Position Variation Using Grammatical Evolution

The majority of existing discrete truss optimization methods focus primarily on optimizing global truss topology using a ground structure approach, in which all possible node and beam locations are specified a priori. The ground structure discrete optimization method has been shown to be restrictive as it limits derivable solutions to what is explicitly defined. Greater representational freedom can improve performance. In this paper, grammatical evolution is applied. It can represent a variable number of nodes and their locations on a continuum. A novel method of connecting evolved nodes using a Delaunay triangulation algorithm shows that fully triangulated, kinematically stable structures can be generated. Discrete beam-truss structures can be optimized without the need for any information about the desired form of the solution other than the design envelope. Our technique is compared to existing discrete optimization techniques, and notable savings in structure self-weight are demonstrated. In particular, our new method can produce results superior to those reported in the literature in cases in which the problem is ill-defined and the structure of the solution is not known a priori.

IEEE Transactions on Evolutionary Computation publication information

A Survey on Evolutionary Computation Approaches to Feature Selection

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

Open Choice

As per our discussion in our recent editorial board meeting, our current model for open access publishing, “Open Choice,” is described here.

Open Choice

As per our discussion in our recent editorial board meeting, our current model for open access publishing, “Open Choice,” is described here.

Renewed Advisory Board and Associate Editors

I am delighted to announce that our Advisory Board has been renewed and now contains the following senior members of our community (with an asterisk marking each new role):

* Wolfgang Banzhaf (on Advisory Board as well as Founding Editor)
* Stephanie Forrest (new to board)
David Goldberg
Erik Goodman
John Koza
* Marc Schoenauer (elevation from regular board member)
* Andy Tyrrell (elevation from Associate Editor)

I am also quite excited about the renewal/expansion of the Associate Editors, including the addition of a new Thematic Area Editor for Software Engineering:

James A. Foster (Area Editor for Life Sciences)
* Mark Harman (new to board, Area Editor for Software Engineering, which is a new Thematic Area)
Hitoshi Iba
Krzysztof Krawiec
William B. Langdon (Resource Review Editor)
Julian Miller
* Alberto Moraglio (elevation from regular board member)
Una-May O’Reilly (Area Editor for Data Analytics and Knowledge Discovery)
* Lukas Sekanina (elevation from regular board member)
Moshe Sipper (Area Editor for Games)
Stephen Smith
Terence Soule
Marco Tomassini
* Martin Trefzer (elevation from regular board member)
Leonardo Vanneschi

I am delighted to announce that our Advisory Board has been renewed and now contains the following senior members of our community (with an asterisk marking each new role):

I am also quite excited about the renewal/expansion of the Associate Editors, including the addition of a new Thematic Area Editor for Software Engineering:

Renewed Advisory Board and Associate Editors

I am delighted to announce that our Advisory Board has been renewed and now contains the following senior members of our community (with an asterisk marking each new role):

I am also quite excited about the renewal/expansion of the Associate Editors, including the addition of a new Thematic Area Editor for Software Engineering:

I am delighted to announce that our Advisory Board has been renewed and now contains the following senior members of our community (with an asterisk marking each new role):

I am also quite excited about the renewal/expansion of the Associate Editors, including the addition of a new Thematic Area Editor for Software Engineering:

Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning

Evolutionary Computation, Volume 24, Issue 3, Page 545-572, Fall 2016. <br/>

Evolutionary Computation, Volume 24, Issue 3, Page 545-572, Fall 2016.

2015 Impact Factor

The 2015 Impact Factor for Genetic Programming and Evolvable Machines is 1.143 (an increase from .903 last year).The 5-year Impact Factor is 1.475.

The 2015 Impact Factor for Genetic Programming and Evolvable Machines is 1.143 (an increase from .903 last year).

The 5-year Impact Factor is 1.475.