It is our great pleasure to welcome you to the 5th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA'15) associated with the 2015 Genetic and Evolutionary Computation Conference (GECCO'15). The ECADA workshop series ...

General-purpose optimization algorithms are often not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved. Hyper-heuristics automate the design of algorithms for a particular ...

Practitioners often need to solve real world problems for which no custom search algorithms exist. In these cases they tend to use general-purpose solvers that have no guarantee to perform well on their specific problem. The relatively new field of ...

Writing parallel programs is a challenging but unavoidable proposition to take true advantage of multi-core processors.

In this paper, we extend Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS) to evolve parallel iterative sorting ...

Hyper-heuristics search the space of heuristics and metaheuristics, so that it can generate high-quality algorithms. It is a growing area of interest in the research community. Algorithms have been constructed iteratively using "templates of operations" ...

Applying evolutionary algorithms to new problem domains is an exercise in the art of parameter tuning and design decisions. A great deal of work has investigated ways to automate the tuning of various EA parameters such as population size, mutation ...

It is our great pleasure to welcome you to the GECCO Workshop on Evolutionary Computation Software Systems (EvoSoft).

The evolution of so many different metaheuristic optimization algorithms results from the fact that no single method can outperform all ...

The ongoing advances in multi-objective optimisation (MOO) are improving the way that complex real-world optimisation problems, mostly characterised by the definition of many conflicting objectives, are currently addressed. To put it into practice, ...

jMetal, an open source, Java-based framework for multi-objective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the last ten years. Our experience using and ...

Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already ...

Ant Colony Optimization (ACO) has become a popular metaheuristic approach for solving hard combinatorial optimization problems. However, most existing ACO software systems are domain-specific, dedicated to concrete problems or non-extensible, non-...

Web browsers have scaled from simple page-rendering engines to operating systems that include most services the lower OS layer has, with the added facility that applications can be run by just visiting a web page. In this paper we will describe the ...

We envisage Genetic Algorithms (GA) as search-based optimisation techniques encompassing independent bio-inspired operators and representations that are realizable as self-contained deployable computational units. In other words, we think of GAs as a ...

Welcome to the GECCO 2015 Black-Box Optimization Benchmarking workshop (BBOB 2015)!

This workshop is a follow up of the BBOB workshops at GECCO 2009 in Montreal, at GECCO 2010 in Portland, at GECCO 2012 in Philadelphia and at GECCO 2013 in Amsterdam as ...

We benchmark IPOP-CMA-ES, a restart Covariance Matrix Adaptation Evolution Strategy with increasing population size, with two step-size adaptation mechanisms, Two-Point Step-Size Adapation (TPA) and Median Success Rule (MSR), on the BBOB noiseless ...

Speeding-up black-box optimization algorithms via learning and using a surrogate model is a heavily studied topic. This paper evaluates two different surrogate models: Gaussian processes and random forests which are interconnected with the state-of-the ...

The recently proposed Brent-STEP algorithm was generalized for separable functions by performing axis-parallel searches, interleaving the steps in individual dimensions in a round-robin fashion. This article explores the possibility to choose the ...

Most surrogate-assisted algorithms for expensive optimization follow the same framework: After an initial design phase in which the true objective function is evaluated for a few search points, an iterative process builds a surrogate model of the ...

It is our great pleasure to welcome you to the GECCO'15 Student Workshop.

This workshop is again organized as a joint event for undergraduate and graduate students. It aims to assist the students with their research in the field of Evolutionary ...

An algorithm inspired in two differential evolution variants is proposed to solve Dynamic Constrained Optimization Problems (DCOPs). It is also added a repair method based on the differential mutation, which does not require feasible solutions as ...

A network is structurally modular if it can be divided into tightly connected groups which are weakly connected or disconnected from each other. Such networks are known to be capable of fast evolutionary adaptation, which makes modularity a highly ...

Efficiency of evolutionary algorithms may be increased using multi-objectivization. Multi-objectivization is performed by adding some auxiliary objectives. We consider selection of these objectives during a run of an evolutionary algorithm.

One of the ...

In this paper we make a detailed computational comparison between different variants of memetic DE approaches, including the two variants Greedy MDE (G-MDE) and Distance MDE (D-MDE), recently introduced in [Cabassi & Locatelli, 2015]. The computational ...

The paper presents a genetic programming based approach for inferring general form Linear Temporal Logic properties of finite-state machine models. Candidate properties are evaluated using several fitness functions, therefore multiobjective evolutionary ...

This paper has been motivated by two observations. First, empirical comparison of algorithms is often carried out in an ad hoc manner. Second, performance data is abundantly generated, yet often not efficiently used. This second observation is ...

Inside a "Bring Your Own Device" environment, the employees can freely use their devices. This allows them mix their personal and work life, but at the same time, if the users are not aware of a risky situation, or that situation is not covered by a ...

A novel approach to produce 2D designs by adapting the HyperNEAT algorithm to evolve non-uniform rational basis splines (NURBS) is presented. This representation is proposed as an alternative to previous pixel-based approaches primarily motivated by ...

As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. To generate offspring, the directed mating utilizes ...

Mission Planning Problem for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features such as the sensors, speed or fuel capacity. ...

Parallelization of fitness evaluation is an established practice in evolutionary computation, and is a necessity in applications where fitness functions are computationally expensive. Traditional master-slave EAs based on a synchronous, generational ...

Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However,...

Grammatical Evolution is an algorithm of Genetic Programming but it is capable of evolving programs in an arbitrary language given by a user-provided context-free grammar. We present a way how to apply Multi-Gene idea, known from Multi-Gene Genetic ...

Designing soft robots is difficult, time-consuming, and non-intuitive. Soft robot design faces two main challenges: structure and control. This research uses generative encodings to grow structures and a vibrational mechanism to control locomotion. In ...

Interactive Evolutionary Computation (IEC) is used in this work in order to perform the optimization of several advertisement blocks of text. The advertisement texts follow a format similar to the one used in a technique called Article Spinning. This ...

Most evolutionary algorithms not only throw out insufficiently good solutions, but forget all information they obtained from their evaluation, which reduces their speed from the information theory point of view. An evolutionary algorithm which does not ...

One of the optimization problems that is widely studied in the literature is the graph coloring problem. In this paper, we present an evolutionary algorithm for the weighted graph coloring problem that combines genetic algorithms with a local search ...

It is our great pleasure to welcome you to the 2015 ACM Workshop on Evolving Collective Behaviors in Robotics, associated with GECCO 2015. This workshop brings together researchers interested in the automatic design of coordinated behaviors in ...

We augment the mEDEA algorithm to explicitly account for the costs of communication between robots. Experimental results show that adding a costs for communication exerts environmental pressure to implicitly select for genomes that maintain high energy ...

In this work, the canonical distributed embodied evolution algorithm used to solve a collective task in which a team of Micro Aerial Vehicles (MAVs) has to do surveillance in an indoor area. In order to efficiently survey the arena, the MAVs need to ...

Generalising on-line learned knowledge in evolutionary robotics results in robots that can accomplish tasks in varying circumstances. This is the goal of the DREAM project. Even faster accomplishment of tasks and understanding of the environment can be ...

There are multiple different ways of implementing artificial evolution of collective behaviors. Besides a classical offline evolution approach, there is, for example, the option of environment-driven distributed evolutionary adaptation in the form of an ...

This workshop presentation describes the general concepts behind embodied evolution, and intends to provide an up- to-date view of lessons learned and current open issues.

Computational capacity and memory are limiting factors when simulating large numbers of robots with complex bodies: available physics engines struggle to handle more than a couple of dozens of complex robot bodies. This limits the possibilities of ...

It is our great pleasure to welcome you to the 2015 Workshop on Metaheuristic Design Patterns -- MetaDeeP '15. This year's workshop follows in the footsteps of the highly successful 2014 workshop on metaheuristic design patterns and continues to provide ...

In interactive metaheuristic search, the human helps to steer the trajectory of the search by providing qualitative evaluation to assist in the selection of solution individuals. It can be challenging to design mechanisms to exploit human qualitative ...

Certain problems have characteristics that present difficulties for metaheuristics: their objective function may be either prohibitively expensive, or they may only give a partial ordering over the solutions, lacking a suitable gradient to guide the ...

We describe the 'Blackboard' design pattern for metaheuristics which allows multiple agents to combine their expertise opportunistically to contribute towards a solution. Features of the Blackboard pattern may include heterogeneity of solution ...

Real world search problems, characterised by nonlinearity, noise and multidimensionality, are often best solved by hybrid algorithms. Techniques embodying different necessary features are triggered at specific iterations, in response to the current ...

It is our great pleasure to welcome you to SMGP 2015, the second edition of the Semantic Methods in Genetic Programming workshop, initiated with the highly successful event we organized at PPSN'14.

Genetic programming (GP)---the application of ...

Semantics has gained much attention in the last few years and new advanced crossover and mutation operations have been created which use semantic information to improve the quality and generalisability of individuals in genetic programming. In this ...

Typically, Genetic Programming (GP) attempts to solve a problem by evolving solutions over a large, and usually pre-determined number of generations. However, overwhelming evidence shows that not only does the rate of performance improvement drop ...

Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal "should-be" values each ...

This study investigates the performance of several semantic- aware selection methods for genetic programming (GP). In particular, we consider methods that do not rely on complete GP semantics (i.e., a tuple of outputs produced by a program for fitness ...

Welcome to MedGEC 2015

MedGEC is the GECCO Workshop on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare.

A dedicated workshop at GECCO continues to provide a much needed focus for medical related ...

Physical activity is recognized as one of the key factors for a healthy life due to its beneficial effects. The range of physical activities is very broad, and not all of them require the same effort to be performed nor have the same effects on health. ...

Traditionally, signal classification is a process in which previous knowledge of the signals is needed. Human experts decide which features are extracted from the signals, and used as inputs to the classification system. This requirement can make ...

Diabetes mellitus is a disease that affects to hundreds of million of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. One of the main problems that arise in the (semi) automatic control of ...

Peptide search engines are algorithms that are able to identify peptides (i.e., short proteins or parts of proteins) from mass spectra of biological samples. These identification algorithms report the best matching peptide for a given spectrum and a ...