The Vehicle Routing Problem (VRP) is a NP-hard and Combinatorial optimization problem. Combinatorial optimization problem can be viewed as searching for best element in a set of discrete items, which can be solved using search algorithm or meta heuristic. In this work, VRP is solved using population based search algorithm, Particle Swarm Optimization (PSO) with crossover and mutation operators....

A hybrid Multi-Objective Evolutionary Algorithm is used to tackle the uncapacitated exam proximity problem. In this hybridization, local search operators are used instead of the traditional genetic recombination operators. One of the search operators is designed to repair unfeasible timetables produced by the initialization procedure and the mutation operator. The other search operator implemen...

In this chapter we present a hybrid evolutionary meta-heuristic based on memetic algorithms (MAs) and several local search algorithms. The memetic algorithm is used as the principal heuristic that guides the search and could use any of 16 local search algorithms during the search process. The local search algorithms used in combination with the MA are obtained by fixing either the type of the n...

Debugging virtual machines (VMs) presents unique challenges, especially meta-circular VMs, which are written in the same language they implement. Making sense of runtime state for such VMs requires insight and interaction at multiple levels of abstraction simultaneously. For example, debugging a Java VM written in Java requires understanding execution state at the source code, bytecode and mach...

The performance of the 3DS3 and 3DS5 3D-SHOTGUN meta-predictors in CAFASP3 is reported. The 3D-SHOTGUN meta-predictors are fully automatic fold recognition servers that attempt to incorporate into the prediction process a number of successful strategies that human predictors often apply. Namely, the input to 3D-SHOTGUN are the top five models predicted by a number of independent fold recognitio...

In combinatorial solution spaces Iterated Local Search turned out to be exceptionally successful. The question arises: is the Iterated Local Search heuristic also able to improve the optimization process in real-valued solution spaces? This paper introduces a hybrid meta-heuristic based on Iterated Local Search and Powell’s optimization method combined with elements from stochastic search in re...

abstract— in this paper we have tried to develop an altered version of the artificial bee colony algorithm which is inspired from and combined with the meta-heuristic algorithm of firefly. in this method, we have tried to change the main equation of searching within the original abc algorithm. on this basis, a new combined equation was used for steps of employed bees and onlooker bees. for this...

Hybrids of meta-heuristics have been shown to be more effective and adaptable than their parents in solving various combinatorial optimization problems. However, hybridized schemes are more tedious to implement due to their complexity. We address this problem by proposing the Meta-heuristics Development Framework (MDF). In addition to being a framework that promotes reuse to reduce developmenta...

This chapter presents a novel framework for tuning the parameters of Evolutionary Algorithms. A hybrid technique combining Meta-EAs and statistical Racing approaches is developed, which is not only capable of effectively exploring the search space of numerical parameters but also suitable for tuning symbolic parameters where it is generally difficult to define any sensible distance metric.

background: to conduct a systematic review and meta-analysis on controlled treatment trials of meta-cognitive therapy for anxiety disorders. materials and methods: studies were included if they employed controlled methodology and treated people above 18 years with anxiety disorders. case studies (with less than 4 cases) and single case designed studies were excluded. a comprehensive literature ...