On Meme Self-Adaptation in Spatially-Structured Multimemetic Algorithms

NMA 2014Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We consider a class of MMAs in which these memes are rewriting rules whose length can be fixed during the run of the algorithm or self-adapt during the search process. We analyze this self-adaptation in the context of spatially-structured MMAs, namely MMAs in which the population is endowed with a certain topology to which interactions (from the point of view of selection and variation operators) are constrained. For the problems considered, it is shown that panmictic (i.e., non-structured) MMAs are more sensitive to this self-adaptation, and that using variable-length memes seems to be a robust strategy throughout different population structures.This work is partially supported by MICINN project ANYSELF (TIN2011-28627-C04-01), by Junta de Andaluía project DNEMESIS (P10-TIC-6083) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

Evolution algorithms in combinatorial optimization

Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not have a major influence. With the availability of parallel computers, these algorithms will become more important. In this paper we discuss the dynamics of three different classes of evolution algorithms: network algorithms derived from the replicator equation, Darwinian algorithms and genetic algorithms inheriting genetic information. We present a new genetic algorithm which relies on intelligent evolution of individuals. With this algorithm, we have computed the best solution of a famous travelling salesman problem. The algorithm is inherently parallel and shows a superlinear speedup in multiprocessor systems

Simulation and Design Optimization of Microsystems Based on Standard Simulators and Adaptive Search Techniques

Abstract The concept of a partial automated design optimization and the improvement of a micropump as a first application is described. Starting with a parametrizable simulation model the parameter values are modified with evolutionary algorithms until the simulation results which describe the behaviour of the system satisfy the defined goals. As the quality of the optimization depends strongly on the quality of the simulation model we give an outlook on a concept for improving the simulation model or components of this model by using FEM-simulation results. Introduction The industrial application of microsystems requires short development times as well as reliable designs comparable to the state of the art in microelectronics. To achieve this goal CAD based design techniques and simulation are necessary but not sufficient as outlined below. During the design process the engineer is faced with an extremely large search space of possible design solutions and parameterizations. Although a great percentage of the design directions can be dismissed based on the available knowledge and the experience gained from previous designs in most cases the remaining search space will be far too large to be systematically investigated. Usually the search process is a trial-and-error process and the result depends on the skill of the engineer but also on luck. This is an unsatisfactory situation and a systematic exploration of the search space will be aspired. Our concept shows a search method for an improved investigation of the design alternatives. One property of an intelligent microsystem is the existence of various physical domains such as microelectronics, micromechanics, microfluidics, microcalorics and microoptics. Several simulators exist to investigate the behaviour of the microsystem but in most cases it is possible to consider only one physical domain of the system. For most microsystems it is not sufficient to investigate and to simulate each domain separatly because the different phenomenas depend strongly on each other. Thus for the investigation of the behaviour of a total microsystem it is necessary either to couple different simulators or to build a model containing the description of all phenomenas in a common description language so that the overall system can be simulated with one simulator. We focus on the latter alternative. An often applied method is the description of the physical effects in analogy to the electronics. This type of model is a network model and can be simulated with a circuit simulator. Analytical studies are necessary to build the model and often it is unavoidable to make simplifications as for example to neglect the friction or to make the assumption that the fluid do not possess a temperature gradient. Depending on the complexity of the system component and its mathematical description the devices of the network can be formulated preferably as usual electronic devices like resistors, capacitors or sources or as HDL-A 1 -models. HDL-A is a trademar