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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 ...
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 ...
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 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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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.
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 ...
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 ...