8187463
Vehicle Routing Problem: Doing It The Evolutionary Way
Some routes in the chromosome may cause the vehicle to exceed its capacity. When this happens, we perform the following modification: the route that exceeds the vehicle capacity is split in several ones. These changes only occur at the interpretation level and, therefore, the information codified in the chromosome is not altered. 3 COEVOLUTIONARY APPROACH We use two subpopulations: individuals from the 1 describe the size of each route; individuals from the 2 specify the order by which the nodes are visited. Routes that exceed vehicle capacity are split.
10111787
Golomb Rulers: The Advantage of Evolution
In this paper we present a new evolutionary algorithm designed to efficiently search for optimal Golomb rulers. The proposed approach uses a redundant random keys representation to codify the information contained in a chromosome and relies on a simple interpretation algorithm to obtain feasible solutions. Experimental results show that this method is successful in quickly identifying good solutions and that can be considered as a realistic alternative to massive parallel approaches that need several months or years to discover high quality Golomb rulers.
12781827
On the influence of GVR in vehicle routing
A comparative study is made between a new evolutionary approach for the Vehicle Routing Problem (VRP) and a standard evolutionary model, based on Path Representation. Genetic Vehicle Representation (GVR) is the new two-level representational scheme designed to deal in an effective way with all the information needed by candidate solutions. Experimental results, obtained from a set of VRP instances, show performance improvements when GVR is used.
2344862
Busy Beaver - The Influence of Representation
The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962, in the context of the existence of non computable functions. In this paper we propose an evolutionary approach to this problem. We will focus on the representational issues, proposing alternative ways of codifying and interpreting Turing Machines, designed to take advantage of the existence of sets of equivalent Turing Machines. The experimental results show that these alternatives provide improvement over the "standard" genetic codification.
42460508
Probabilistic Evolution and the Busy Beaver Problem
9947181
The Influence of Learning in the Evolution of Busy Beavers
The goal of this research is to study how individual learning interacts with an evolutionary algorithm in its search for good candidates for the Busy Beaver problem. Two learning models, designed to act as local search procedures, are proposed. Experimental results show that local search methods that are able to perform several modifications in the structure of an individual in each learning step provide an important advantage. Some insight about the role that evolution and learning play during search is also presented.
36300286
REBUILDER: A CBR Approach to Knowledge Management in Software Design
Knowledge generated during the software development process can be a valuable asset for a software company. But in order to take advantage of this knowledge, the company must be able to store this knowledge for later use, which can be achieved through the use of knowledge management tools. This paper provides an overview of a computational system for management and reuse of software design knowledge. We present a Case-Based Reasoning approach combined with a lexical resource: WordNet. We explain how knowledge management is performed and how is the stored knowledge reused.
13936575
Shallow Parsing with Conditional Random Fields
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.
11219736
Combining Case-Based Reasoning and Analogical Reasoning in Software Design
Designers use several types of knowledge and reasoning mechanisms during the creation of new artefacts. In order to cope with this cognitive characteristic of design, an intelligent design tool able to help a designer must integrate several reasoning mechanisms and knowledge formats. Case-based reasoning and analogical reasoning are usually considered as two distinct mechanisms, though they are also considered to be in the same cognitive axis, case-based reasoning being in one extreme, and analogy in the other. Both are important reasoning mechanisms in the generation of new designs, but they both reflect different ways of exploring the design space. In this paper we present a way of combining both techniques, showing how it was integrated in an intelligent software design tool. Experimental results are presented and discussed, showing the advantages and limitations of this approach.
18854826
Case-Based Reuse of UML Diagrams
Software reuse has the potential of reducing development time and increasing software quality. There are several types of knowledge that can be reused, with code being the most common reused one. Code reuse is the most direct and easier type of reuse, but is not the most efficient one. Design reuse is more productive than code reuse, because changes at the design level can have big effects on the implementation level, thus compromising code reuse. This paper presents an approach to reuse of software designs, which are in the form of UML class diagrams. The presented approach is based on Case-Based Reasoning providing a framework for storing, retrieving, creating and revising class diagrams.
16219760
Selection and Reuse of Software Design Patterns Using CBR and WordNet
Software engineers and programmers deal with repeated problems and situations in the course of software design. This lead to the development of software design patterns, which can be defined as a description of an abstract solution for abstract design problems. Existing approaches to pattern application using computer tools, need the help and guidance of a human designer to select which design pattern to apply. The automation of this task opens the possibility of CASE design tools providing complete automation for the application of design patterns, and the offering new functionalities that can help the software designer to improve systems, and do better software reuse. In this paper we present an approach that automates design pattern selection and application. This approach is based on CaseBased Reasoning and WordNet, showing how they are combined to generate evolved software design diagrams. We also present an experimental study of our approach.