Dept. of ECE, Univ. of California, San Diego

9500 Gilman Drive                           

La Jolla, CA 92093-4007                     

Ph: (619) 534-5935; Fax: (619) 534-1004     

kchellap@ece.ucsd.edu  

Gary Fogel

Natural Selection Inc., La Jolla, CA 92037

1. Computer Experiments on the Development of Niche Specialization in an Artificial Ecosystem

Jon Brewster and Michael Conrad

Dept. of Computer Science

Wayne State University

Detroit, MI 48202

Evolve IV is the most recent of the EVOLVE series of evolutionary ecosystem models. The key new feature is all interactions among organisms are mediated by metabolic transformations. The model is thus well suited to the study of niche proliferation since evolutionary pressures lead to specialization and mutually stimulatory relationships in a natural way.

2. Multiple RNA Sequence Alignment Using Evolutionary Programming

Kumar Chellapilla and Gary Fogel

Multiple sequence alignment and comparison are popularly used techniques for the identification of common structure among ordered strings of nucleotides (as is the case with DNA or RNA) or amino acids (as is the case with proteins). Current multiple sequence alignment algorithms are characterized by great computational complexity. The focus of this paper is to use evolutionary programming as the basis for developing an efficient multiple sequence alignment algorithm for RNA sequences. An evolutionary programming (EP) based multiple sequence alignment algorithm is presented. Variants on the basic algorithm are also presented and their trade-offs are discussed. The appropriate weights associated with matches, mismatches and gaps are critical for the success of any algorithm. Simulation results on the canonical version indicate that the proposed EP method is not only viable but offers a much more robust alternative to conventional methods as the differences in the structure increase.

3. When Metaphors Collide: Biological Underpinnings To Genetic Programming Theory

Jason Daida

University of Michigan, Artificial Intelligence Lab & Space Physics

Research Lab, 2455 Hayward Ave, Ann Arbor, MI 48109-2143 USA,

(734) 647-4581 (wk), (734) 764-5137 (fax), daida@eecs.umich.edu (email),

Current theoretical research in genetic programming (GP) owes part of its heritage to the use of biological metaphor. Indeed, one could go even further by stating that biological metaphors have significantly shaped the direction of theoretical development in GP. Whether in the particulars of what in GP maps to genotype and to phenotype, or in the wholes of what in GP maps to a set of dynamics that is reflective of some biological phenomena, the use of biology in framing GP theory has been pervasive. However, current research also shows that descriptions of GP dynamics can deviate significantly from what one would expect in biology. Therefore, our investigations have lead us to rethink how the biology maps to processes and artifacts in GP. Consequently, this paper discusses how the use of biological metaphor can over constrain critical portions in the development of GP theory.

4. Simulated Sequencing by Hybridization Using Evolutionary Programming

Gary Fogel and Kumar Chellapilla

Sequencing of DNA is among the most important tasks in molecular biology. DNA chips are considered to be a more rapid alternative to more common gel-based methods for sequencing. Previously, we demonstrated the reconstruction of DNA sequence information from a simulated DNA chip using evolutionary programming. The research presented here extends this work by relaxing several assumptions required in our initial investigation. We also examine the relationship between base composition of the target sequence and the useful set of probes required to decipher the target on a DNA chip. Comments regarding the nature of the optimal ratio for the target and probe lengths are offered. Our results go further to suggest that evolutionary computation is well suited for the sequence reconstruction problem.

5. A Survey of Recent Work on Evolutionary Approaches to the Protein Folding Problem.

Garrison Greenwood* , Byungkook Lee**, Jae-Min Shin**, and Gary Fogel***

*Dept. of Electrical & Computer Engineering, Western Michigan University,

Kalamazoo, MI 49008

** Laboratory of Molecular Biology, National Cancer Institute, Bethesda, MD

20892

*** Natural Selection Inc., La Jolla, CA 92037

A problem of immense importance in computational biology is the determination of the function conformations of protein molecules. With the advent of faster computers, it is now possible to use heuristic algorithms to search conformation space for protein structures that have minimal free-energy. Surveys work done in the last five years using evolutionary search algorithms to find low energy protein conformations. In particular, a detailed description is included of some work recently started at the National Cancer Institute, which uses evolution strategies.

6. Creation Of A Biomimetic Dolphin Hearing Model Through The Use Of Evolutionary Computation

D. S. Houser1, D. A. Helweg, and P. W. B. Moore

1SPAWARSYSCEN-San Diego, Code D351, 49620 Beluga Road, San Diego,

CA 92152-5435

Niche exploitation by an organism cumulatively results from its existing adaptations and phylogenetic history. The biological sonar of dolphins is an adaptation for object (e.g. prey or obstacle) detection and classification in visually limited environments. The unparalleled echo discrimination capability of the dolphin provides an excellent model for investigating similar synthetic systems. Current biomimetic modeling of dolphin echo discrimination emphasizes the mechanical and neurological filtering of the peripheral auditory system prior to central nervous system processing of echoes. Psychoacoustic, anatomical, and neurophysiological data collected from the bottlenose dolphin (Tursiops truncatus) indicate the structure of some auditory tuning curves. However, an optimal filter set has yet to be developed that demonstrates comparable frequency-dependent sensitivity. Evolutionary computation techniques have been employed to optimize the sensitivity of filters to that observed in the bottlenose dolphin by seeding the population with known filter parameters and evolving the number, frequency distribution, and shape of individual filters. Comparisons of evolved and known biological tuning curves will be discussed.

7. Evolutionary Computation Enhancement of Olfactory System Model

Mary Lou Padgett and Gerry V. Dozier, Auburn University

Recent electron microscopy work on the anatomy of the olfactory system in the rat has suggested a structural basis for grouping input stimuli before processing to classify odors. In the construction of a simulated nose, the appropriate number of inputs per group is a design parameter which can be fine-tuned using evolutionary computation. Previous results indicate that improvements to classification accuracy can be made by grouping inputs. On the other hand, the cost of such grouping can potentially increase the number of samples required for each "sniff". This increase can be expensive in terms of hardware and processing time. This paper suggests a methodology for selecting a size range based on improvement in accuracy and cost of grouping inputs.