Mateen M. Rizki

Computing the theory of evolution

Abstract The sources of diversity in evolutionary ecosystems are investigated using a discrete event model (EVOLVE III). This interactional model incorporates realistic features of organisms, including complex phenotypic traits and a detailed representation of genetic organization. The model differs from traditional models by allowing the fitness of an organism to be an emergent property of the ecosystem. Local interactions between pairs of organisms determine the fitness of the individual. Experimental results are presented for simulation runs that competed generalist and specialist populations in a gradient free environment. These experiments reveal three sources of diversity: transient polymorphisms, selection driven polymorphisms, and character displacement. A principle of equality of maximum resource utilization is proposed as a major factor in the process of diversification.

Differential binding between volatile ligands and major urinary proteins due to genetic variation in mice

Two different structural classes of chemical signals in mouse urine, i.e., volatile organic compounds (VOCs) and the major urinary proteins (MUPs), interact closely because MUPs sequester VOCs. Although qualitative and/or quantitative differences in each chemical class have been reported, previous studies have examined only one of the classes at a time. No study has analyzed these two sets simultaneously, and consequently binding interactions between volatile ligands and proteins in urines of different strains have not been compared. Here, we compared the release of VOCs in male urines of three different inbred strains (C57BL/6J, BALB/b and AKR) before and after denaturation of urinary proteins, mainly MUPs. Both MUP and VOC profiles were distinctive in the intact urine of each strain. Upon denaturation, each of the VOC profiles changed due to the release of ligands previously bound to MUPs. The results indicate that large amounts of numerous ligands are bound to MUPs and that these ligands represent a variety of different structural classes of VOCs. Furthermore, the degree of release in each ligand was different in each strain, indicating that different ligands are differentially bound to proteins in the urines of different strains. Therefore, these data suggest that binding interactions in ligands and MUPs differ between strains, adding yet another layer of complexity to chemical communication in mice.

E-Net: Evolutionary neural network synthesis

Abstract E-Net is a new distributed evolutionary learning system that evolves neural-network-based pattern recognition systems (PRSs) with limited human interaction. This system orchestrates a multiplicity of evolutionary and classical learning techniques to synthesize feature detectors, select sets of cooperative features, and assemble classifiers. Feature detectors are represented as feed-forward neural networks and recognition systems are defined using a collection of networks. E-Net evolves network topologies and trains weights to form accurate recognition systems using a computationally efficient process that gradually extends primitive network topologies to form increasingly discriminating structures. The evolutionary search process effectively explores the space of candidate topologies by manipulating populations of feature detectors and recognition systems using variation operators such as crossover and mutation. The majority of evolutionary learning techniques have been designed to perform parameter optimization. E-Net is designed to perform both synthesis and optimization. Consequently, many novel concepts and techniques are introduced in this research that expedite the gradual synthesis of structure, such as the new multitiered selection process used in E-Nets evolutionary algorithm that avoids premature convergence to complex topological structures.

The artificial worlds approach to emergent evolution

Artificial worlds models of evolutionary systems are computer models that map the essential logical structure of ecological systems, defined as self-sustaining biological organizations. The artificial world comprises an artificial environment, with mass components, energy input, and physical states. It also comprises artificial organisms, including a genome, a phenome, and a (developmental) map that connects the genome to the phenome. Mass components are cycled and space is limited. The evolution process results, as in nature, from genetic variation combined with natural selection imposed by the finiteness of the environment. The selection criteria (fitness values) are not imposed, but rather emerge from the interactions of the organisms with each other and with the environment. The dynamics at the population level also emerges from these basic interactions. In this paper we describe the comparative properties of the EVOLVE family of artificial worlds models.

Changes in volatile compounds of mouse urine as it ages: Their interactions with water and urinary proteins

Mice release a variety of chemical signals, particularly through urine, which mediate social interactions and endocrine function. Studies have been conducted to investigate the stability of urinary chemosignals in mice. Neuroendocrine and behavioral responses of mice to urine samples of male and female conspecifics which have aged for different amounts of time have been examined, demonstrating that the quality and intensity of signaling molecules in urine change over time. In this study, we monitored changes in volatile organic compounds (VOCs) released from male and female mouse urine following aging the urine samples. Substantial amounts of some VOCs were lost during the aging process of urine, whereas other VOCs increased. Considerable portions of the VOCs which exhibited the increased release were shown to have previously been dissolved in water and subsequently released as the urine dried. We also demonstrated that some VOCs decreased slightly due to their binding with the major urinary proteins (MUPs) and identified MUP ligands whose headspace concentrations increased as the urine aged. Our results underscore the important role of MUPs and the hydration status in the release of VOCs in urine, which may largely account for the changes in the quality and intensity of urinary signals over time.

Metabolite Differentiation and Discovery Lab (MeDDL): A New Tool for Biomarker Discovery and Mass Spectral Visualization

The goal of this work was to design and implement a prototype software tool for the visualization and analysis of small molecule metabolite GC-MS and LC-MS data for biomarker discovery. The key features of the Metabolite Differentiation and Discovery Lab (MeDDL) software platform include support for the manipulation of large data sets, tools to provide a multifaceted view of the individual experimental results, and a software architecture amenable to modification and addition of new algorithms and software components. The MeDDL tool, through its emphasis on visualization, provides unique opportunities by combining the following: easy use of both GC-MS and LC-MS data; use of both manufacturer specific data files as well as netCDF (network Common Data Form); preprocessing (peak registration and alignment in both time and mass); powerful visualization tools; and built in data analysis functionality.

Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems

Abstract This paper describes one aspect of a machine-learning system called HELPR that blends the best aspects of different evolutionary techniques to bootstrap-up a complete recognition system from primitive input data. HELPR uses a multi-faceted representation consisting of a growing sequence of non-linear mathematical expressions. Individual features are represented as tree structures and manipulated using the techniques of genetic programming. Sets of features are represented as list structures that are manipulated using genetic algorithms and evolutionary programming. Complete recognition systems are formed in this version of HELPR by attaching the evolved features to multiple perceptron discriminators. Experiments on datasets from the University of California at Irvine (UCI) machine-learning repository show that HELPR’s performance meets or exceeds accuracies previously published.

Performance-driven autonomous design of pattern-recognition systems

The closed-loop design experiment described in this paper demonstrates a three-phase automated design approach to pattern recognition. The experiment generates morphological feature detectors and then uses a novel application of genetic algorithms to select cooperative sets of features to pass to a neural net classifier. The self-organizing hybrid learning approach embodied in this closed-loop design methodology is complementary to conventional artificial intelligence (AI) expert systems that utilize rule-based approaches and a specific set of design elements. This experiment is part of a study directed to emulating the nondirected processes of biological evolution. The approach we discuss is semiautomatic in that initialization of computer programs requires human experience and expertise to select representations, develop search strategies, choose performance measures, and devise resource-allocation strategies. The hope is that these tasks will become easier with experience and will provide the means to ...

Automatic generation of morphological sequences

Morphological sequences (algorithms or programs) are generated using an evolutionary approach. A population of morphological sequences is manipulated and expanded in discrete steps. At each time-step two tasks are initiated--program discovery and program construction. The discovery phase searches for short morphological sequences which extract novel features. Program composition utilizes these sequences, which are partial solutions, to form increasingly effective sequences. The composition phase selects pairs of sequences and combines them into extended sequences which capture spatial relationships. The enhanced population serves as the basis for another phase of discovery and composition. Several demonstrations illustrate the systems ability to synthesize and integrate feature extraction routines.

An evolutionary learning system for synthesizing complex morphological filters

This paper describes a system based on evolutionary learning, called MORPH, that semi-automates the generation of morphological programs. MORPH maintains a population of morphological programs that is continually enhanced. The first phase of each learning cycle synthesizes morphological sequences that extract novel features which increase the populations diversity. The second phase combines these newly formed operator sequences into larger programs that are better than the individual programs. A stochastic selection process eliminates the poor performers, while the survivors serve as the basis of another learning cycle. Experimental results are presented for binary and grayscale target recognition problems.