The vibrant city of Madrid will be the venue of GECCO where, as usual, in the first two days will be offered an attractive selection of tutorials covering a wide variety of themes. After a record of 56 submissions from international, high profile domain ...

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 ...

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 ...

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. ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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,...

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. ...