Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This chapter investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework o...

Evolutionary algorithms and hill-climbing search models are investigated to address the protein structure prediction problem. This is a well-known NP-hard problem representing one of the most important and challenging problems in computational biology. The pull move operation is engaged as the main local search operator in several approaches to protein structure prediction. The considered appro...

Hill climbing algorithms can train neural control systems for adaptive agents. They are an alternative to gradient descent algorithms especially if neural networks with non-layered topology or non-differentiable activation function are used, or if the task is not suitable for backpropagation training. This paper describes three variants of generic hill climbing algorithms which together can tra...

Genetic Algorithms are biologically inspired optimization algorithms. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Crossover operators are used to bring diversity in the population. This paper studies different crossover operators and then proposes a hybrid crossover operator that incorporates knowledg...

This paper introduces an approach to locating dependence structures in a program by searching the space of the powerset of the set of all possible program slices. The paper formulates this problem as a search based software engineering problem. To evaluate the approach, the paper introduces an instance of a search based slicing problem concerned with locating sets of slices that decompose a pro...

This paper presents an Improved Memetic Algorithm (IMA) designed to compute near-optimal solutions for the antibandwidth problem. It incorporates two distinguishing features: an efficient heuristic to generate a good quality initial population and a local search operator based on a Stochastic Hill Climbing algorithm. The most suitable combination of parameter values for IMA is determined by emp...

Prostate cancer is the most common cancer among men in developed countries. Androgen deprivation therapy (ADT) is the standard treatment for prostate cancer. However, approximately one third of all patients with metastatic disease treated with ADT develop resistance to ADT. This condition is called metastatic castrate-resistant prostate cancer (mCRPC). Patients who do not respond to hormone the...

Our project attempted to use machine learning to recreate or redefine features of an image using an arrangement of transparent overlapping polygons. From genetic algorithm[1] and hill climbing[2], we came up an implementation that starts by generating a random sequence of polygons then iteratively mutating the sequence (slightly modifying a random attribute of a random polygon), incrementally b...

In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using a random walk and a hill climbing components. Furthermore, one of the previous PSO approaches is improved i...

With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term “prototypes” refers to the reference instances used in a nearest neighbor computation — the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely ...