Evolutionary Computation, Ahead of Print.
On Constructing Ensembles for Combinatorial Optimisation
Evolutionary Computation, Ahead of Print. <br/>
The LCS and GBML community stop
Evolutionary Computation, Ahead of Print. <br/>
Evolutionary Computation, Ahead of Print.
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,…
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.
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,…
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.
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
Evolutionary Computation, Ahead of Print. <br/>
Evolutionary Computation, Ahead of Print.
Evolutionary Computation, Ahead of Print. <br/>
Evolutionary Computation, Ahead of Print.
Evolutionary Computation, Ahead of Print. <br/>
Evolutionary Computation, Ahead of Print.
Evolutionary Computation, Volume 24, Issue 4, Page 573-575, Winter 2016. <br/>
Evolutionary Computation, Volume 24, Issue 4, Page 573-575, Winter 2016.
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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 ta…
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.