Recently we have observed, that behavior of many contemporary logic synthesis and optimization processes depends on variable ordering in their input; they produce different results for different variable orderings. This fact can be exploited to escape local optima in the iterative resynthesis process, where individual synthesis and optimization steps are run repeatedly, in order to gradually im...

Random taxation may be optimal when the tax payers differ in their attitudes towards risk, so that tax randomization enables the government to separate agents who otherwise would have been pooled. The paper provides conditions under which randomization is optimal, both characterizing circumstances where local random deviations are improving in a neighborhood of a non random optimum, and giving ...

The problem of coordinating the actions of individual processors is fundamental in distributed computing. Researchers have long endeavored to nd eecient solutions to a variety of problems involving coordination. Recently, processor knowledge has been used to characterize such solutions and to derive more eecient ones. Most of this work has concentrated on the relationship between common knowled...

The graph coloring problem is often investigated in the literature. Many insights about many neighboring solutions with the same fitness value are raised but as far as we know, no deep analysis of this neutrality has ever been conducted in the literature. In this paper, we quantify the neutrality of some hard instances of the graph coloring problem. This neutrality property has to be detected a...

Particle swarm optimization (PSO) has been initially proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change over time. Thanks to its stochastic and population based nature, PSO can avoid being trapped in local optima and find the global optimum. However, this is...

Budgeted Red-Blue Median is a generalization of classic k-Median in that there are two sets of facilities, say R and B, that can be used to serve clients located in some metric space. The goal is to open kr facilities in R and kb facilities in B for some given bounds kr, kb and connect each client to their nearest open facility in a way that minimizes the total connection cost. We extend work b...

Over recent years, optimisation and evolutionary search have seen substantial interest in the MDE research community. Many of these techniques require the specification of an optimisation problem to include a set of model transformations for deriving new solution candidates from existing ones. For some problems—for example, planning problems, where the domain only allows specific actions to be ...

Local search heuristics are among the most popular approaches to solve hard optimization problems. Among them, Very Large Scale Neighborhood Search techniques present a good balance between the quality of local optima and the time to search a neighborhood. We develop a language to generate exponentially large neighborhoods for sequencing problems using grammars. We develop efficient generic dyn...

In this paper, we discuss the dual-problem of adjusting the mixture number and avoiding local optima in the estimation of a Gaussian mixture. This estimation is widely used in unsupervised-classification applications; however, its results are serially sensitive to the initial setting, which is difficult to optimize. It is also difficult to automatically designate the mixture number in advance. ...

Budgeted Red-Blue Median is a generalization of classic k-Median in that there are two sets of facilities, say R and B, that can be used to serve clients located in some metric space. The goal is to open kr facilities in R and kb facilities in B for some given bounds kr, kb and connect each client to their nearest open facility in a way that minimizes the total connection cost. We extend work b...