18592363
Evolving Golomb Rulers
A Golomb Ruler is defined as a ruler that has marks unevenly spaced at integer locations in such a way that the distance between any two marks is unique. Unlike usual rulers, they have the ability to measure more discrete measures than the number of marks they possess. Although the definition of a Golomb Ruler does not place any restriction on the length of the ruler, researchers are usually interested in rulers with minimum length. An Optimal Golomb Ruler (OGR) is defined as the shortest length ruler for a given number of marks [1].
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
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.
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.
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.
11245975
Experiments with free concept generation in Divago
This paper presents a set of experiments we carried out with, Divago, a system that is an attempt to implement our ideas towards a computational model of creativity. It is expected to be able to generate novel concepts out of previous knowledge. Here we show its behaviour with a large dataset constructed independently by other researchers consisting of over 170 nouns (for a project named C^3). Each noun is represented with a syntax that is equivalent to the one adopted for Divago. We apply a two step experimentation procedure, which starts by ''training'' the system with ''preferred outcomes'' and then allowing it to do free generation, constrained by the pragmatic goal of a given query. We evaluate the results and make a short discussion regarding well-defined criteria of novelty and usefulness. We also present a comparison with a similar experiment done with C^3.
43422353
Manufacturing Automation Network's Cooperative E-Space
Associating people into cooperative networks can be beneficial for both the individual and the community. It can occur by sharing expertise, vision and talent of different participants. Manufacturing Automation Network - Manet - is a Brazilian academic and research network that pools together human and material resources of academic institutions and research centers active in the fields of manufacturing and automation. Currently, a logical and computing infrastructure is being developed to make the Manet operation feasible through the Internet. A distributed infrastructure, named Cooperative e-Space - CeS, is being designed offer to people that already participate in cooperative network, a new infrastructure for sharing information, taking into consideration the special characteristics of their community, in a way that can lead to knowledge sharing.
30550943
Reuse of UML Class Diagrams Using Case-Based Composition
7441581
Quantifiers, Anaphora, and Intensionality
The relationship between Lexical-Functional Grammar (LFG) functional structures (f-structures) for sentences and their semanticinterpretations can be formalized in linear logic in a way thatcorrectly explains the observed interactions between quantifier scopeambiguity, bound anaphora and intensionality.Our linear-logic formalization of the compositional properties ofquantifying expressions in natural language obviates the need forspecial mechanisms, such as Cooper storage, in representing thescoping possibilities of quantifying expressions. Instead, thesemantic contribution of a quantifier is recorded as a linear-logicformula whose use in a proof will establish the scope of thequantifier. Different proofs can lead to different scopes. In eachcomplete proof, the properties of linear logic ensure thatquantifiers are properly scoped.The interactions between quantified NPs and intensional verbs such as’’seek‘‘ are also accounted for in this deductive setting. A singlespecification in linear logic of the argument requirements ofintensional verbs is sufficient to derive the correct readingpredictions for intensional-verb clauses both with nonquantified andwith quantified direct objects. In particular, both de dictoand de re readings are derived for quantified objects. Theeffects of type-raising or quantifying-in rules in other frameworksjust follow here as linear-logic theorems.While our approach resembles current categorial approaches inimportant ways (Moortgat, 1988, 1992a; Carpenter, 1993; Morrill, 1994)it differs from them in allowing the greater compositional flexibility ofcategorial semantics (van Benthem, 1991)while maintaining a precise connection to syntax. As a result, we areable to provide derivations for certain readings of sentences withintensional verbs and complex direct objects whose derivation inpurely categorial accounts of the syntax-semantics interface appearsto require otherwise unnecessary semantic decompositions of lexicalentries.
5245012
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finite-state conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.