Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

The investigation of the reduction mechanism of the 5a-reductase enzyme of Penicillium decumbens /

The 5a-reductase of Penicillium decumbens ATCC 10436 was used as a model for the mammalian enzyme to investigate the mechanism of reduction of testosterone to 5adihydrotestosterone . The purpose of this study was to search for specific 5a-reductase inhibitors which antagonize prostate cancer . In a whole-cell biotransformation mode, this organism reduced testosterone (1) to 5a-dihydrosteroids (8) and 5aandrostane- 3, 17-dione (9) in yields of 28% and 37% respectively. Control experiments have shown that 5aandrostane- 3, 17-dione (9) can be produced from the corresponding alcohol (8) in a subsequent reaction separate from that catalysed by the 5a-reductase enzyme . Androst-4- ene-3, 17-dione (2) is reduced to give only (9) with a recovery of 80% The stereochemistry of the reduction was determined by 500 MHz ^H NMR analysis of the products resulting from the deuterium labelled substrates. The results were obtained by an analysis of the NOE difference spectra, double-quantum filtered phase sensitive COSY 2-D spectra, and ^^c-Ir 2-D shift correlation spectra of deuterium labelled products. According to the unambiguous assignment of the signals due to H-4a and H-4Ii in 5a-dihydro steroids, the NMR data show clearly that addition of hydrogen to the 4{5)K bond has occurred in a trans manner at positions 413 and 5a. To Study the reduction mechanism of this enzyme, several substrates were prepared as following; 3-methyleneandrost-4-en- 17fi-ol(3), androst-4-en-17i5-ol(5) , androst-4-en-3ii, 17fi-diol (6) and 4, 5ii-epoxyandrostane-3, 17-dione (7) . Results suggest that this enzyme system requires an oxygen atom at the 3-position of the steroid in order to bind the substrate. Furthermore, the mechanism of this 5a-reductase may proceed via direct addition of hydrogen at the 4,5 position without involvement of a carbonyl group as an intermediate.