This year's Late-Breaking Abstracts Workshop continues its tradition of presenting late-breaking developments in the field of genetic and evolutionary computation at GECCO. The aim is to provide a forum for late-breaking results that were not available ...

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) is composed of two interacting sub- problems which are the mode assignment problem and the scheduling problem. Solving these sub-problems in isola- tion pose a challenge due to the ...

We present a novel approach to the study of cognitive abilities by using evolutionary computation. To this end we use a spatial, developmental, neuroevolution system presented here for the first time. We use our system to evolve ANNs to perform simple ...

Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider ...

This paper deals with real world application of the Multiple depot vehicle routing problem. In fact, we focus on a transportation system called Personal Rapid Transit problem(PRT) in order to minimize total energy consumption in a multiple depot network ...

The solution of large NP-hard optimization problems is traditionally approached with heuristic methods, which can provide satisfactory solutions with low execution times, but without guaranteeing the eventual evaluation of all the possible solutions. ...

Within the vehicle routing problem literature, the distance constrained vehicle routing problem (DCVRP) has its is importance due to its practical relevance of logistics operation. However, the DCVRP with the objective of minimizing the total traveling ...

A malicious botnet is a collection of compromised hosts coordinated by an external entity. The malicious software, or malware, that infect the systems are its basic units and they are responsible for its global behavior. Anti Virus software and ...

Surrogate-assisted Evolutionary Computation provides us good results in real-world optimization. In this paper, we propose a novel adaptive evolution control using P-I similarity index for surrogate-assisted EC. The computational experiments are carried ...

In the past, evolved virtual creatures (EVCs) have been developed with rigid, segmented bodies, and with soft bodies, but never before with a combination of the two. In nature, however, creatures combining a rigid skeleton and non-rigid muscles are some ...

In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the ...

This paper describes a novel way to develop trading rules with a trend following philosophy, combining several time-frames which average the Rate of Return of the several components and decrease the Maximum Drawdown. This represents strategy ...

We present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as "similar solutions have similar fitness". We explore definitions of this concept in terms of metrics ...

Natural Evolution has been exploiting the physical properties of matter since life first appeared on earth. Evolution-in-materio (EIM) attempts to program matter so that computational problems can be solved. The beauty of this approach is that ...

The time complexity of problems and algorithms, i.e., the scaling of the time required for solving a problem instance as a function of instance size, is of key interest in theoretical computer science and practical applications. This paper presents an ...

A portfolio of parameter-less evolutionary algorithms called Parameter-less Evolutionary Portfolio is proposed. This portfolio implements a heuristic that performs adaptive selection of parameter-less evolutionary algorithms in accordance with ...

This work focuses on representative MOEA/D and propose a method to efficiently evaluate solutions in parallel. The proposed parallel MOEA/D assigns the computational resources to generate solutions in the minimum overlapped update ranges of solutions. ...

This paper proposes parallelization methods of ACO algorithms on a multi-core processor aiming at fast execution to find acceptable solutions. As an ACO algorithm, we use the cAS (cunning Ant System) and test on several sizes of Quadratic Assignment ...

This study proposes a novel grammar guided Genetic Programming method to solve a real world problem, the Storage Location Assignment Problem (SLAP) with Grouping Constraints. Self-adaptive Tabu Search algorithms are evolved by this approach and it can ...

This paper proposes an artificial fish swarm clustering algorithm based on dynamic niche (FSDN), and a dynamic identification of the niches is performed at each generation to automatically evolve the optimal number of clusters as well as the cluster ...