We consider the applicability of stochastic global optimization algorithms on test-functions whose domain of definition is a simply-connected and finite interval of real numbers. We argue on the basis of theoretical reflections of statistical physics (namely random-walk) and computer simulations...

Global optimization (GO) is one of the key numerical tools in computational physics. Among the GO algorithms the ones originating in statistical physics are particularly powerful. Recently an adaptive scheme was developed to increase the efficiency of one of these algorithms (stochastic...

In this paper, we introduce a generalized proximal Lagrangian function for the constrained nonlinear programming problem and discuss existence of its saddle points. In particular, the local saddle point is obtained by using the second-order sufficient conditions, and the global saddle point is...

Fitness landscape theory is a mathematical framework for numerical analysis of search algorithms on combinatorial optimization problems. We study a representation of fitness landscape as a weighted directed graph. We consider out forest and in forest structures in this graph and establish...

The application of evolution-inspired strategies to hardware design and circuit self-configuration leads to the concept of evolvable hardware (EHW). EHW refers to self-configuration of electronic hardware by evolutionary/genetic algorithms (EA and GA, respectively). Unconventional circuits, for...

In this paper we propose a statistical physics approach to experimental results on bacterial mutations (Escherichia coli). We get scaling laws that describe some generic traits and suggest some features of the underlying dynamical structure for the considered evolution process. Our main...

Evolution has created the complexity of the animate world and deciphering the language of evolution is the key toward understanding nature. The dynamics of evolution is simplified by considering it as a superposition of three less sophisticated processes: population dynamics, population support...

In this paper we develop a new primal-dual subgradient method for nonsmooth convex optimization problems. This scheme is based on a self-concordant barrier for the basic feasible set. It is suitable for finding approximate solutions with certain relative accuracy. We discuss some applications of...

In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem....