Computational processes of evolution and the gene expression messy genetic algorithm

This paper makes an effort to project the theoretical lessons of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework introduced elsewhere (Kargupta, 1995b) in the context of natural evolution and introduce the gene expression messy genetic algorithm (GEMGA) -- a new generation of messy GAs that directly search for relations among the members of the search space. The GEMGA is an O({vert_bar}{Lambda}{vert_bar}{sup k}({ell} + k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995a) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length {ell} and alphabet set {Lambda}. Unlike the traditional evolutionary search algorithms, the GEMGA emphasizes the computational role of gene expression and uses a transcription operator to detect appropriate relations. Theoretical conclusions are also substantiated by experimental results for large multimodal problems with bounded inappropriateness of representation

Gene expression: The missing link in evolutionary computation

This paper points out that the traditional perspective of evolutionary computation may not provide the complete picture of evolutionary search. This paper focuses on gene expression-- transformations of representation (DNA->RNA->Protein) from a the perspective of relation construction. It decomposes the complex process of gene expression into several steps, namely (1) expression control of DNA base pairs, (2) alphabet transformations during transcription and translation, and (3) folding of the proteins from sequence representation to Euclidean space. Each of these steps is investigated on grounds of relation construction and search efficiency. At the end these pieces of the puzzle are put together to develope a possibly crude and cartoon computational description of gene expression

From DNA to protein: Transformations and their possible role in linkage learning

This paper first extends the traditional perspective of linkage using the basic concepts developed in the SEARCH framework and identifies the fundamental objectives of linkage learning. It then explores the computational role of gene-expression (DNA{r_arrow}RNA{r_arrow}Protein transformations) in evolutionary linkage learning, using group representation theory. It offers strong evidence to support the hypothesis that the transformations in gene-expression define a group of symmetry transformations that leaves the fitness invariant; however, they change the eigen functions leading to identifying independent subspaces of the search space (a major objective of linkage learning) using irreducible representations of such transformations

Extending the class of order-k delineable problems for the gene expression messy genetic algorithm

This paper revisits the recently introduced gene expression messy genetic algorithm (GEMGA) and offers some modifications to the extend the class of order-k delineable problems (class of problems that can be solved using a bounded order of relations) in GEMGA. The fundamental components that control the delineability of relations are reviewed in the light of the recently proposed SEARCH framework. Modified class and relation comparison statistics of GEMGA are proposed. The sample complexity of this improved version of GEMGA is shown to be subquadratic. Theoretical conclusions are also substantiated by experimental results for large, multimodal order-k delineable problems with respect to class average comparison statistic. We also present results for the recently constructed Goldberg-Lewei test functions

Redundant Representations in Evolutionary Computation

Redundanz , Evolutionary programmin

Constrained blackbox optimization: The SEARCH perspective

Search and optimization in the context of blackbox objective function evaluation subject to blackbox constraints satisfaction is the thesis of this work. The SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework introduced by Kargupta (1995) offered an alternate perspective of blackbox optimization in terms of relations, classes, and partial ordering. The primary motivation comes from the observation that sampling in blackbox optimization is essentially an inductive process and in the absence of any relation among the members of the search space, induction is no better than enumeration. SEARCH also offers conditions for polynomial complexity search and bounds on sample complexity using its ordinal, probabilistic, and approximate framework. In this work the authors extend the SEARCH framework to tackle constrained blackbox optimization problems. The methodology aims at characterizing the search domain into feasible and infeasible relations among which the feasible relations can be explored further to optimize an objective function. Both -- objective function and constraints -- can be in the form of blackboxes. The authors derive results for bounds on sample complexity. They demonstrate their methodology on several benchmark problems

PADMA: PArallel Data Mining Agents for scalable text classification

This paper introduces PADMA (PArallel Data Mining Agents), a parallel agent based system for scalable text classification. PADMA contains modules for (1) parallel data accessing operations, (2) parallel hierarchical clustering, and (3) web-based data visualization. This paper introduces the general architecture of PADMA and presents a detailed description of its different modules

Credit card fraud detection: An application of the gene expression messy genetic algorithm

This paper describes an application of the recently introduced gene expression messy genetic algorithm (GEMGA) (Kargupta, 1996) for detecting fraudulent transactions of credit cards. It also explains the fundamental concepts underlying the GEMGA in the light of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) (Kargupta, 1995) framework

Templeting of Thin Films Induced by Dewetting on Patterned Surfaces

The instability, dynamics and morphological transitions of patterns in thin liquid films on periodic striped surfaces (consisting of alternating less and more wettable stripes) are investigated based on 3-D nonlinear simulations that account for the inter-site hydrodynamic and surface-energetic interactions. The film breakup is suppressed on some potentially destabilizing nonwettable sites when their spacing is below a characteristic lengthscale of the instability, the upper bound for which is close to the spinodal lengthscale. The thin film pattern replicates the substrate surface energy pattern closely only when, (a) the periodicity of substrate pattern matches closely with the characteristic lengthscale, and (b) the stripe-width is within a range bounded by a lower critical length, below which no heterogeneous rupture occurs, and an upper transition length above which complex morphological features bearing little resemblance to the substrate pattern are formed.Comment: 5 pages TeX (REVTeX 4), other comments: submitted to Phys. Rev.Let

Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data

The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by the data owner. In the latter case, usually some servers are hired to perform the task of clustering. The dataset is divided by the data owner among the servers who together perform the k-means and return the cluster labels to the owner. The major challenge in this method is to prevent the servers from gaining substantial information about the actual data of the owner. Several algorithms have been designed in the past that provide cryptographic solutions to perform privacy preserving k-means. We provide a new method to perform k-means over a large set using multiple servers. Our technique avoids heavy cryptographic computations and instead we use a simple randomization technique to preserve the privacy of the data. The k-means computed has exactly the same efficiency and accuracy as the k-means computed over the original dataset without any randomization. We argue that our algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems Security. Springer, Cham, 201