3,123 research outputs found
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
Evolving text classification rules with genetic programming
We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications
On the use of semantic awareness to limit overfitting in genetic programming
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsMachine learning and statistics provide powerful tools to solving problems of
many different shapes. But with the algorithms searching for approximations the
problem of overfitting remains present. Genetic Programming describes an algorithmic
approach that is likely to produce overfitting solutions. Thus, in order
to lessen the risk of overfitting and increasing the generalization ability of genetic
programming the use of semantic information is assessed in different ways.
A multi-objective system driving the population away from overfitting solutions
based on semantic distance is presented alongside alternatives and extensions.
The extensions include the use of the semantic signature to increase the amount
of information available to the system, as well as the consideration to replace the
validation dataset. It is on the one hand concluded that the described approaches
and none of the extensions have a positive impact on the generalization ability.
But on the other hand it seems that the semantics do contain enough information
to appropriately discriminate between overfitting and not overfitting individuals
Optimal advertising campaign generation for multiple brands using MOGA
The paper proposes a new modified multiobjective
genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting
the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these
results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using
the real data sets from the Canadian advertising industry
Model refactoring using examples: a searchâbased approach
One of the important challenges in modelâdriven engineering is how to improve the quality of the models' design in order to help designers understand them. Refactoring represents an efficient technique to improve the quality of a design while preserving its behavior. Most of existing work on model refactoring relies on declarative rules to detect refactoring opportunities and to apply the appropriate refactorings. However, a complete specification of refactoring opportunities requires a huge number of rules. In this paper, we consider the refactoring mechanism as a combinatorial optimization problem where the goal is to find good refactoring suggestions starting from a small set of refactoring examples applied to similar contexts. Our approach, named model refactoring by example, takes as input an initial model to refactor, a set of structural metrics calculated on both initial model and models in the base of examples, and a base of refactoring examples extracted from different software systems and generates as output a sequence of refactorings. A solution is defined as a combination of refactoring operations that should maximize as much as possible the structural similarity based on metrics between the initial model and the models in the base of examples. A heuristic method is used to explore the space of possible refactoring solutions. To this end, we used and adapted a genetic algorithm as a global heuristic search. The validation results on different systems of realâworld models taken from openâsource projects confirm the effectiveness of our approach. Copyright © 2014 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108085/1/smr1644.pd
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
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