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The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance, which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions.

Trust is a fundamental concept that underpins the coherence and resilience of social systems and shapes human behavior. Despite the importance of trust as a social and psychological concept, the concept has not gained much attention from evolutionary game theorists. In this letter, an ${N}$ -player trust-based social dilemma game is introduced. While the theory shows that a society with no untrustworthy individuals would yield maximum wealth to both the society as a whole and the individuals in the long run, evolutionary dynamics show this ideal situation is reached only in a special case when the initial population contains no untrustworthy individuals. When the initial population consists of even the slightest number of untrustworthy individuals, the society converges to zero trusters, with many untrustworthy individuals. The promotion of trust is an uneasy task, despite the fact that a combination of trusters and trustworthy trustees is the most rational and optimal social state. This letter presents the game and results of replicator dynamics in a hope that researchers in evolutionary games see opportunities in filling this critical gap in the literature.

This paper presents a modified ant colony optimization (ACO) approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the concerned problem: 1) a multidimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the capability of heuristic search (neighboring area search); 3) a tabu-table-based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; and 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ACO can well exploit the global and local information of routing-related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the integrated five extended mechanisms, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.

Provides a listing of the editors, board members, and current staff for this issue of the publication.

Until now, numerous mutation strategies have been introduced as search operators within the differential evolution (DE) algorithm. These operators are designed mainly to improve fitness value while also maintaining diversity in the population, but they do not directly act to reduce constraint violations of constrained problems. Interestingly, the so-called constraint handling techniques, used with most evolutionary algorithms, are not a part of the actual search process. Instead, the constraint violations are only considered in the ranking and selection of individuals for participation in the search process. This paper introduces a new DE mutation operator that incorporates a mechanism, based on constraint consensus, that can directly help to reduce the constraint violations during the evolutionary search process. The proposed DE algorithm has been tested on a set of well-known constrained benchmark problems. The experimental results show that the proposed algorithm is able to obtain better solutions, compared to the standard DE algorithm, with significantly reduced computational effort. The algorithm also outperforms state-of-the-art algorithms.

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation

Call for Papers

Guest Editors:

Dr. Su Nguyen, Victoria University of Wellington, New Zealand (su.nguyen@ecs.vuw.ac.nz)

Dr. Yi Mei, Victoria University of Wellington, New Zealand (yi.mei@ecs.vuw.ac.nz)

Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand (Mengjie.Zhang@ecs.vuw.ac.nz)

For a formatted PDF version of this CFP click here.

Scheduling and combinatorial optimisation problems appear in many practical applications in production and service industries and have been the research interest of researchers from operations research and computer science. These problems are usually challenging in terms of both complexity and dynamic changes, which requires the development of innovative solution methods. Although the research in this field has made a lot of progress, designing effective algorithms/heuristics for scheduling and combinatorial optimisation problems is still a hard and tedious task. In the last decade, there has been a growing interest in applying computational intelligence (particularly evolutionary computation) techniques to help facilitate the design of scheduling algorithms and many state-of-the-art methods have been developed.

This special issue aims to present the most recent advances in scheduling and combinatorial optimisation with a special focus on automated heuristic design and self-adaptive algorithms. This includes (1) offline approaches to automatically discover new and powerful algorithms/heuristics for scheduling and combinatorial optimisation problems, and (2) online approaches which allow scheduling algorithms to self- adapt during the solving process. We encourage papers employing variable-length representations for scheduling algorithms. Here are a number of potential techniques which are highly relevant to this special issue:

– Hyper-heuristics for heuristic/operator selection
– Hyper-heuristics for generating new operators and algorithms
– Memetic algorithms
– Genetic programming
– Evolutionary design of heuristics
– Self-adaptive evolutionary algorithms
– Machine learning-based meta-heuristics
– Learning classifier systems
– Scheduling or optimisation of algorithms and machines

Topics of interest include, but are not limited to:

– Production scheduling
– Timetabling
– Vehicle routing
– Grid/cloud scheduling
– 2D/3D strip packing
– Space/resource allocation
– Automated heuristic design
– Innovative applications of scheduling and combinatorial optimisation
– Web service composition
– Wireless networking state location allocation
– Airport runway scheduling
– Project scheduling
– Traffic control

Paper Submission

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.

Manuscripts should be submitted to: http://GENP.edmgr.com.

Choose “Automated Design and Adaptation” as the article type when submitting.

Important Dates

Submission deadline: Oct. 1, 2016 Extended to October 15, 2016
Notification of first review:December 1, 2016
Resubmission: January 20, 2017
Final acceptance notification: February 20, 2017

References

[1] J. Branke, S. Nguyen, C. W. Pickardt, and M. Zhang, “Automated Design of Production Scheduling Heuristics: A Review,” IEEE Trans. Evol. Comput., vol. 20, no. 1, pp. 110–124, Feb. 2016.
[2] E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. R. Woodward, “Exploring Hyper-heuristic Methodologies with Genetic Programming,” in Computational Intelligence, vol. 1, C. Mumford and L. Jain, Eds. Springer Berlin Heidelberg, 2009, pp. 177–201.
[3] L. Feng, Y.-S. Ong, M.-H. Lim, and I. W. Tsang, “Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP,” IEEE Trans. Evol. Comput., vol. 19, no. 5, pp. 644–658, Oct. 2015.
[4] G. Kendall and N. M. Hussin, “A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology,” in Practice and Theory of Automated Timetabling V, vol. 3616, E. Burke and M. Trick, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 270–293.
[5] N. R. Sabar, M. Ayob, G. Kendall, and Rong Qu, “Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems,” IEEE Trans. Evol. Comput., vol. 19, no. 3, pp. 309–325, Jun. 2015.
[6] J. H. Drake, E. Özcan, and E. K. Burke, “A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem,” Evol. Comput., vol. 24, no. 1, pp. 113–141, Mar. 2016.

Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation

Call for Papers

Guest Editors:

Dr. Su Nguyen, Victoria University of Wellington, New Zealand (su.nguyen@ecs.vuw.ac.nz)

Dr. Yi Mei, Victoria University of Wellington, New Zealand (yi.mei@ecs.vuw.ac.nz)

Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand (Mengjie.Zhang@ecs.vuw.ac.nz)

For a formatted PDF version of this CFP click here.

Scheduling and combinatorial optimisation problems appear in many practical applications in production and service industries and have been the research interest of researchers from operations research and computer science. These problems are usually challenging in terms of both complexity and dynamic changes, which requires the development of innovative solution methods. Although the research in this field has made a lot of progress, designing effective algorithms/heuristics for scheduling and combinatorial optimisation problems is still a hard and tedious task. In the last decade, there has been a growing interest in applying computational intelligence (particularly evolutionary computation) techniques to help facilitate the design of scheduling algorithms and many state-of-the-art methods have been developed.

This special issue aims to present the most recent advances in scheduling and combinatorial optimisation with a special focus on automated heuristic design and self-adaptive algorithms. This includes (1) offline approaches to automatically discover new and powerful algorithms/heuristics for scheduling and combinatorial optimisation problems, and (2) online approaches which allow scheduling algorithms to self- adapt during the solving process. We encourage papers employing variable-length representations for scheduling algorithms. Here are a number of potential techniques which are highly relevant to this special issue:

– Hyper-heuristics for heuristic/operator selection
– Hyper-heuristics for generating new operators and algorithms
– Memetic algorithms
– Genetic programming
– Evolutionary design of heuristics
– Self-adaptive evolutionary algorithms
– Machine learning-based meta-heuristics
– Learning classifier systems
– Scheduling or optimisation of algorithms and machines

Topics of interest include, but are not limited to:

– Production scheduling
– Timetabling
– Vehicle routing
– Grid/cloud scheduling
– 2D/3D strip packing
– Space/resource allocation
– Automated heuristic design
– Innovative applications of scheduling and combinatorial optimisation
– Web service composition
– Wireless networking state location allocation
– Airport runway scheduling
– Project scheduling
– Traffic control

Paper Submission

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.

Manuscripts should be submitted to: http://GENP.edmgr.com.

Choose “Automated Design and Adaptation” as the article type when submitting.

Important Dates

Submission deadline: Oct. 1, 2016
Notification of first review:December 1, 2016
Resubmission: January 20, 2017
Final acceptance notification: February 20, 2017

References

[1] J. Branke, S. Nguyen, C. W. Pickardt, and M. Zhang, “Automated Design of Production Scheduling Heuristics: A Review,” IEEE Trans. Evol. Comput., vol. 20, no. 1, pp. 110–124, Feb. 2016.
[2] E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. R. Woodward, “Exploring Hyper-heuristic Methodologies with Genetic Programming,” in Computational Intelligence, vol. 1, C. Mumford and L. Jain, Eds. Springer Berlin Heidelberg, 2009, pp. 177–201.
[3] L. Feng, Y.-S. Ong, M.-H. Lim, and I. W. Tsang, “Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP,” IEEE Trans. Evol. Comput., vol. 19, no. 5, pp. 644–658, Oct. 2015.
[4] G. Kendall and N. M. Hussin, “A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology,” in Practice and Theory of Automated Timetabling V, vol. 3616, E. Burke and M. Trick, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 270–293.
[5] N. R. Sabar, M. Ayob, G. Kendall, and Rong Qu, “Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems,” IEEE Trans. Evol. Comput., vol. 19, no. 3, pp. 309–325, Jun. 2015.
[6] J. H. Drake, E. Özcan, and E. K. Burke, “A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem,” Evol. Comput., vol. 24, no. 1, pp. 113–141, Mar. 2016.