This work aims to investigate the problems of evaluating expressions in the string format in the .NET framework. The performances of several mathematical parser libraries in .NET are measured and compared. An alternative approach based on a dynamic code compilation is presented. The standard benchmark functions for optimization are used to compare existing libraries against a dynamic code compi...

This paper introduces a methodology for soil slope stability analysis based on optimization, limit equilibrium principles and method of slices. In this study, the slope stability analysis problem is transformed into a constrained nonlinear optimization problem. To solve that, a Self-Adaptive Genetic Algorithm (GA) is utilized. In this study, the slope stability safety factors are the objective ...

The aim of this work is to benchmark scoring functions used by Bayesian network learning algorithms in the context of classification. We considered both information-theoretic scores, such as LL, AIC, BIC/MDL, NML and MIT, and Bayesian scores, such as K2, BD, BDe and BDeu. We tested the scores in a classification task by learning the optimal TAN classifier with benchmark datasets. We conclude th...

Differential evolution (DE) is a popular meta-heuristic optimizer which has shown good performance in solving many real-life and benchmark optimization problems. However, DE usually shows slow convergence rate at the last stage of the evolution. To enhance the performance of DE, this paper proposed an improved DE variant (OLSDE) which employs opposition-based concept and local search strategy. ...

In this paper we introduce a nondeterministic counterpart to Reduced, Ordered Binary Decision Diagrams for the representation and manipulation of logic functions. ROBDDs are conceptually related to deterministic finite automata (DFA), accepting the language formed by the minterms of a function. This analogy suggests the use of nondeterministic devices as language recognizers. Unlike ROBDDs, the...

A general non-asymptotic framework, which evaluates the performance of any procedure at individual functions, is introduced in the context of estimating convex functions at a point. This framework, which is significantly different from the conventional minimax theory, is also applicable to other problems in shape constrained inference. A benchmark is provided for the mean squared error of any e...

Bio-inspired algorithms are algorithms inspired in the nature commonly used for solving optimization problems. A class of the bioinspired optimization algorithms is swarm algorithms which mimic the collective behavior in animals. An example is Particle Swarm Optimization (PSO) based in the social behavior of bird flocking. This paper presents a variation on the basic PSO algorithm, called A2PSO...

Alopex is a correlation-based algorithm, which shares characteristics of both gradient descent approach and simulated annealing. It has been successfully applied to continuous and combinatorial optimization problems for years. Estimation of Distribution Algorithms (EDAs) is a class of novel evolutionary algorithms (EAs) proposed in recent years. Compared with the traditional EAs, it possesses u...

In this paper we reduce a free boundary problem from heat transfer to a weakly Singular Volterra  integral equation of the first kind. Since the first kind integral equation is ill posed, and an appropriate method for such ill posed problems is based on wavelets, then we apply the Chebyshev wavelets to solve the integral equation. Numerical implementation of the method is illustrated by two ben...