420093
Substructural Surrogates for Learning Decomposable Classification Problems
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model, which represents salient interactions between attributes for a given data, (2) a surrogate model, which provides a functional approximation of the output as a function of attributes, and (3) a classification model, which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate. Its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, groups them in linkages groups, and builds maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and also shed light on several improvements to enhance the capabilities of the proposed method.
7338878
Speeding online synthesis via enforced selecto-recombination
Brainstorming has been greatly used as a method to generate a large number of ideas by variety of each participant's knowledge. However, brainstorming does not always work well because of spatial and communication limitations. Moreover, brainstorming techniques present limited scalability. Meanwhile, genetics algorithms have been mostly regarded as an engineering or technological tool. However, the innovation intuition suggests that genetic algorithms may be also regarded as models of human innovation and creativity. This paper focuses on online creativity sessions. Modeling those creative efforts using selecto-recombinative mechanism can provide three times more novel ideas, increase the posting frequency by a 2.6 factor, and help overcome superficiality on online communications by favoring synthetic thinking.
11347478
Fluctuating Crosstalk as a Source of Deterministic Noise and Its Effects on GA Scalability
This paper explores how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. We model fluctuating crosstalk or nonlinear interactions among building blocks via higher-order Walsh coefficients. The fluctuating crosstalk behaves like exogenous noise and can be handled by increasing the population size and run duration. This behavior holds until the strength of the crosstalk far exceeds the underlying fitness variance by a certain factor empirically observed. Our results also have implications for the relative performance of building-block-wise mutation over crossover.
20147111
Improving small population performance under noise with viral infection + tropism
In this paper we report on the effect of viral infection with tropism on the formation of building blocks in genetic operations. In previous research, we applied genetic algorithms to the analysis of time-series signals with noise. We demonstrated the possibility of reducing the number of required entities and improving the rate of convergence when searching for a solution by having some of the host chromosomes harbor viruses with a tropism function. Here, we simulate problems having both multimodality and deceptiveness features and problems that include noise as test functions, and show that viral infection with tropism can increase the proportion of building blocks in the population when it cannot be assumed that a necessary and sufficient number of entities are available to find a solution. We show that this capability is especially noticeable in problems that include noise.
46040947
Genetic Algorithm Difficulty and the Modality of Fitness Landscapes
Abstract We assume that the modality (i.e., number of local optima) of a fitness landscape is related to the difficulty of finding the best point on that landscape by evolutionary computation (e.g., hillclimbers and genetic algorithms (GAs)). We first examine the limits of modality by constructing a unimodal function and a maximally multimodal function. At such extremes our intuition breaks down. A fitness landscape consisting entirely of a single hill leading to the global optimum proves to be harder for hillclimbers than GAs. A provably maximally multimodal function, in which half the points in the search space are local optima, can be easier than the unimodal, single hill problem for both hillclimbers and GAs. Exploring the more realistic intermediate range between the extremes of modality, we construct local optima with varying degrees of “attraction” to our evolutionary algorithms. Most work on optima and their basins of attraction has focused on hills and hillclimbers, while some research has explored attraction for the GA's crossover operator. We extend the latter results by defining and implementing maximal partial deception in problems with k arbitrarily placed global optima. This allows us to create functions, such as the minimum distance function f mdG , with k isolated global optima and multiple local optima attractive to both crossover and hillclimbers. The function f mdG seems to be a powerful new tool for generalizing deception and relating hillclimbers (and Hamming space) to GAs and crossover.
42677379
Generalized State Values in an Anticipatory Learning Classifier System
This paper introduces generalized state values to the anticipatory learning classifier system ACS2. Previous studies showed that the evolving generalized state value in ACS2 might be overgeneral for a proper policy representation. Thus, the policy representation is separated from the model representation. A function approximation module is added that approximates state values. Actual action choice then depends on the learned generalized state values predicted by the means of the predictive model yielding anticipatory behavior. It is shown that the function approximation module accurately generalizes the state value function in the investigated MDP. Improvement of the approach by the means of further anticipatory interaction between predictive model learner and state value learner is suggested. We also propose the implementation of task dependent anticipatory attentional mechanisms exploiting the representation of the generalized state-value function. Finally, the anticipatory framework may be extended to support multiple motivations integrated in a motivational module which could be influenced by emotional biases.
60847515
Two analysis tools to describe the operation of classifier systems
Anti-Microbial Quaternary Ammonium Co-Polymers formed by the condensation of at least two di-functional tertiary amines and a molar quantity of 1,4-dihalo-2-butene that is equal to the molar sum of the di-functional tertiary amines in the mixture.