Abstract. Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew. This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences. 1.

Abstract—Abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the most probable configurations given observed evidence. When we are interested only in a subset of the network’s variables, this problem is called partial abductive inference. Both problems are NP-hard and so exact computation is not always possible. In this paper, a genetic algorithm is used to perform partial abductive inference in BBNs. The main contribution is the introduction of new genetic operators designed specifically for this problem. By using these genetic operators, we try to take advantage of the calculations previously carried out, when a new individual is evaluated. The algorithm is tested using a widely used Bayesian network and a randomly generated one and then compared with a previous genetic algorithm based on classical genetic operators. From the experimental results, we conclude that the new genetic operators preserve the accuracy of the previous algorithm, and also reduce the number of operations performed during the evaluation of individuals. The performance of the genetic algorithm is, thus, improved. Index Terms—Abductive inference, bayesian belief networks, evolutionary computation, genetic operators, most probable explanation, probabilistic reasoning. I.

The role of software is becoming increasingly important in the implementation of DSP applications. As this trend intensifies, and the complexity of applications escalates, we are seeing an increased need for automated tools to aid in the development of DSP software. This paper reviews the state of the art in programming language and compiler technology for DSP software implementation. In particular, we review techniques for high level, block-diagram-based modeling of DSP applications; the translation of block diagram specifications into efficient C programs using global, target-independent optimization techniques; and the compilation of C programs into streamlined machine code for programmable DSP processors, using architecture-specific and retargetable back-end optimizations. In our review, we also point out some important directions for further investigation.

The term evolutionary computation encompasses a host of methodologies inspired by natural evolution that are used to solve hard problems. This paper provides an overview of evolutionary computation as applied to problems in the medical domains. We begin by outlining the basic workings of six types of evolutionary algorithms: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems. We then describe how evolutionary algorithms are applied to solve medical problems, including diagnosis, prognosis, imaging, signal processing, planning, and scheduling. Finally, we provide an extensive bibliography, classified both according to the medical task addressed and according to the evolutionary technique used.

In this paper, we present a modular co-synthesis framework called CHARMED that solves the problem of hardware-software co-synthesis of periodic, multi-mode, distributed, embedded systems. In this framework we perform the synthesis under several constraints while optimizing for a set of objectives. We allow the designer to fully control the performance evaluation process, constraint parameters, and optimization goals. Once the synthesis is performed, we provide the designer a non-dominated set (Pareto front) of implementations on streamlined architectures that are in general heterogeneous and distributed. We also employ two different techniques, namely clustering and parallelization, to reduce the complexity of the solution space and expedite the search. The experimental results demonstrate the effectiveness of the CHARMED framework in computing efficient co-synthesis solutions within a reasonable amount of time. 1.

The cumulative step-size adaptation (CSA) based on path length control is regarded as a robust alternative to the standard mutative self-adaptation technique in evolution strategies (ES), guaranteeing an almost optimal control of the mutation operator. In this short paper it is shown that the underlying basic assumption in CSA -- the perpendicularity of expected consecutive steps -- does not necessarily guarantee optimal progress performance for (=I ; ) intermediate recombinative ES.

Abstract—Multiprocessor mapping and scheduling algorithms have been extensively studied over the past few decades and have been tackled from different perspectives. In the late 1980’s, the two-step decomposition of scheduling—into clustering and clusterscheduling—was introduced. Ever since, several clustering and merging algorithms have been proposed and individually reported to be efficient. However, it is not clear how effective they are and how well they compare against single-step scheduling algorithms or other multistep algorithms. In this paper, we explore the effectiveness of the two-phase decomposition of scheduling and describe efficient and novel techniques that aggressively streamline interprocessor communications and can be tuned to exploit the significantly longer compilation time that is available to embedded system designers. We evaluate a number of leading clustering and merging algorithms using a set of benchmarks with diverse structures. We present an experimental setup for comparing the single-step against the two-step scheduling approach. We determine the importance of different steps in scheduling and the effect of different steps on overall schedule performance and show that the decomposition of the scheduling process indeed improves the overall performance. We also show that the quality of the solutions depends on the quality of the clusters generated in the clustering step. Based on the results, we also discuss why the parallel time metric in the clustering step may not provide an accurate measure for the final performance of cluster-scheduling. Index Terms—Interprocessor communication, multiprocessor systems, scheduling, task partitioning. 1

In this work we apply statistical learning methods in the context of combinatorial optimization, which is understood as nding a binary string minimizing a given cost function. We rst consider probability densities over binary strings and we dene two dierent statistical criteria. Then we recast the initial problem as the problem of nding a density minimizing one of the two criteria. We restrict ourselves to densities described by a small number of parameters and solve the new problem by means of gradient techniques. This results in stochastic algorithms which iteratively update density parameters. We apply these algorithms to two families of densities, the Bernoulli model and the Gaussian model. The algorithms have been implemented and some experiments are reported. 1 Introduction In this work, we apply statistical learning methods in the context of combinatorial optimization, which is understood as nding a binary string minimizing a given cost function. We transform t...

It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m...

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