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Detailed Programme

Bio-inspired Algorithms for Continuous Parameter Optimisation

''The main application areas of EC techniques [in industry] are multi-objective optimization, classification, data mining and numerical optimization''. [1]

Many engineering problems of both theoretical and practical interest involve choosing the best configuration of a set of parameters to achieve a specified objective. Numerical optimisation refers to the case when these parameters take continuous real values, as opposed to combinatorial optimisation, which deals with discrete values. Examples include designing production processes for maximum efficiency, optimal parameter adjustment for controllers and many others. EvoNUM focuses on such problems.

We seek high quality papers involving the application of bio-inspired algorithms (genetic algorithms, genetic programming, evolution strategies, differential evolution, particle swarm optimization, evolutionary programming, simulated annealing… and their hybrids) to continuous optimisation problems in engineering. We also welcome cross-fertilisation between Nature-inspired algorithms and more classical numerical optimisation algorithms.

[1] GS Hornby & T Yu, "EC Practitioners: Results of the First Survey", SIGEVOlution, Newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, Vol. 2(1), Spring 2007 www.sigevolution.org

Areas of Interest and Contributions

EvoNUM deals with engineering applications where continuous parameters or functions have to be optimised, in fields such as control, chemistry, agriculture, electricity, building and construction, energy, aerospace engineering, design optimisation, etc.

EvoNUM aims to cover areas that include but are not limited to:


Accepted papers will appear in the proceedings of EvoStar, published in a volume of the Springer Lecture Notes in Computer Science, which will be available at the Conference.Submissions must be original and not published elsewhere. The submissions will be peer reviewed by at least three members of the program committee. The authors of accepted papers will have to improve their paper on the basis of the reviewers comments and will be asked to send a camera ready version of their manuscripts. At least one author of each accepted work has to register for the conference and attend the conference and present the work.The reviewing process will be double-blind, please omit information about the authors in the submitted paper.

Submission Details

Submissions must be original and not published elsewhere. They will be peer reviewed by members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper.

Submit your manuscript in Springer LNCS format.

Please provide up to five keywords in your Abstract

Page limit: 12 pages to http://myreview.csregistry.org/evoapps14/.


Submission deadline: 1 November 2013 11 November 2013
Notification: 06 January 2014
Camera ready: 01 February 2014
EvoNUM: 23-25 April 2014


Further information on the conference and co-located events can be
found in: http://www.evostar.org

Programme Committee

EvoNUM Programme

Thurs 0930-1110  EvoNUM 
Chair: Anna I Esparcia-Alcázar

A Novel Genetic Algorithmic Approach for Computing Real Roots of a Nonlinear Equation     Vijaya Lakshmi V. Nadimpalli, Rajeev Wankar, Raghavendra Rao Chillarige
Novel Pre-processing and Post-processing methodologies are designed to enhance the performance of the classical Genetic Algorithms (GA) approach so as to obtain efficient interval estimates in finding the real roots of a given nonlinear equation. The Pre-processing methodology suggests a mechanism that adaptively fixes the parameter-‘length of chromosome’ in GA. The proposed methodologies have been implemented and demonstrated through a set of benchmark functions to illustrate the effectiveness.

A Multi-Objective Relative Clustering Genetic Algorithm with Adaptive Local/Global Search based on Genetic Relatedness     Iman Gholaminezhad, Giovanni Iacca
This paper describes a new evolutionary algorithm for multi-objective optimization, namely Multi-Objective Relative Clustering Genetic Algorithm (MO-RCGA), inspired by concepts borrowed from gene relatedness and kin selection theory. The proposed algorithm clusters the population into different families based on individual kinship, and adaptively chooses suitable individuals for reproduction. The idea is to use the information on the position of the individuals in the search space provided by such clustering schema to enhance the convergence rate of the algorithm, as well as improve its exploration. The proposed algorithm is tested on ten unconstrained benchmark functions proposed for the special session and competition on multi-objective optimizers held at IEEE CEC 2009. The Inverted Generational Distance (IGD) is used to assess the performance of the proposed algorithm, in comparison with the IGD obtained by state-of-the-art algorithms on the same benchmark.
Noisy Optimization: Convergence with a Fixed Number of Resamplings     Marie-Liesse Cauwet
It is known that evolution strategies in continuous domains might not converge in the presence of noise. It is also known that, under mild assumptions, and using an increasing number of resamplings, one can mitigate the effect of additive noise and recover convergence. We show new sufficient conditions for the convergence of an evolutionary algorithm with constant number of resamplings; in particular, we get fast rates (log-linear convergence) provided that the variance decreases around the optimum slightly faster than in the so-called multiplicative noise model. Keywords: Noisy optimization, evolutionary algorithm, theory.

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms     Giovanni Iacca, Ferrante Neri, Fabio Caraffini, Ponnuthurai Nagaratnam Suganthan

The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework co- operatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms.