Hierarchical Problem Solving and the Bayesian Optimization Algorithm
The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimization algorithm (BOA) is extended in order to solve the proposed class of problems.

title: Communication Gap Management for Fertile Community
abstract: In the paper, we first present an approach to extract social networks from message boards on the Internet. Then we propose communication gaps based on structural features of the social networks as an indicator of understanding the state of communication. After we classify 3,000 social networks into three types of communication, i.e., interactive communication, distributed communication, and soapbox communication, we suggest communication gap management to identify the types of communication, the roles of individuals, and important ties, all of which can be used for drawing up a plan for realizing fertile community.

title: SEARCH, Blackbox Optimization, And Sample Complexity
abstract: The SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework developed elsewhere (Kargupta, 1995; Kargupta and Goldberg, 1995) offered an alternate perspective toward blackbox optimization -- optimization in presence of little domain knowledge. The SEARCH framework investigates the conditions essential for transcending the limits of random enumerative search using a framework developed in terms of relations, classes and partial ordering. This paper presents a summary of some of the main results of that work. A closed form bound on the sample complexity in terms of the cardinality of the relation space, class space, desired quality of the solution and the reliability is presented. This also leads to the identification of the class of order-k delineable problems that can be solved in polynomial sample complexity. These results are applicable to any blackbox search algorithms, including evolutionary optimization techniques.

title: Fluctuating Crosstalk as a Source of Deterministic Noise and Its Effects on GA Scalability
abstract: 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.

title: Substructural Surrogates for Learning Decomposable Classification Problems
abstract: 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.

title: 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.

title: Two analysis tools to describe the operation of classifier systems
abstract: 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.