Abstract-Constraint handling is not straightforward in evolutionary algorithms (ea) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous eas for solving constraint satisfaction problems (csp) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these eas on a systematically generated test suite of random binary csps. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (ec) field.

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when Manuscript a participant was listening continuously to the long piece of naturalistic music.

Evolutionary algorithms (EAs) for solving con straint satisfaction problems (CSPs) can be roughly di vided into two classes: EAs using adaptive fitness func tions and EAs using heuristics. In [8] the most effective EAs of the first class have been compared experimentally using a large set of benchmark instances consisting of ran domly generated binary CSPs. In this paper we complete this comparison by studying the most effective EAs of the second class. We test three heuristic based EAs on the same benchmark instances used in [8]. The results of our experiments indicate that the three heuristic based EAs have similar performance on random binary CSPs. More over, comparing these results with those in [8], we are able to identify the best EA for binary CSPs as the algorithm introduced in [3] which uses a heuristic as well as an adap tive fitness function. 1 Introd uction Constraint satisfaction is a fundamental topic in artificial in telligence with relevant applications in planning, default rea soning, scheduling, etc. Informally, a constraint satisfaction problem (CSP) consists of finding an assignment of values to variables in such a way that the restrictions imposed by the constraints are satisfied. CSPs are, in general, computation ally intractable (NP-hard) and the algorithms that solve them can be divided into two classes: the ones that are tailored to solve a specific CSP and the ones that use 'rules-of-thumb' or heuristics to solve them. Although heuristics do not guar antee successful performance, they are able to produce an an swer in a very short time and are used to guide the algorithm through the search space. Evolutionary algorithms (EAs) for CSPs can be divided into two classes: EAs using adaptive fitness functions ([1, 3, 4, 6, 7, 11, 17, 18]) and EAs using

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