Multi-population methods with adaptive mutation for multi-modal optimization problems

open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Affirmative Action and Diversity in Public Education — Legal Developments

The first part of this report briefly reviews the judicial evolution of race-based affirmative action, particularly in relation to public education. Recent rulings challenging the use of racial admissions and hiring practices by public educational institutions are then considered for their implications on the future development of affirmative action law

Subsidizing Start-Ups: Policy Targeting and Policy Effectiveness

Start-up subsidies are a frequently employed policy instrument, the use of which is justified by alleged market failure resulting from positive external effects and capital market imperfections. This article investigates whether the allocation of subsidies reflects a policy focus on addressing these market failure occurrences. However, using survey data from the East German state of Thuringia, logistic regressions reveal a rather random subsidization of start-ups. Furthermore, propensity score matching suggests that subsidized start-ups would have survived and thrived in any case, an indication of deadweight losses of start-up subsidies. The analysis points to serious information problems arising when subsidies should be allocated to remedy market failure. Making the situation even more problematic is that failure to precisely target start-up subsidies is likely to result in market distortions and ineffectiveness.Start-ups, Subsidies, Subsidy allocation, Policy evaluation

The Bayesian boom: good thing or bad?

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition

The EU and social protection: What should the European Convention propose?

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