112 research outputs found
Formation of modules in a computational model of embryogeny
An investigation is conducted into the effects of a complex mapping between genotype and phenotype upon a simulated evolutionary process. A model of embryogeny is utilised to grow simple French flag like patterns. The system is shown to display a phenotypic robustness to damage and it is argued that this is a result of a modularity forming within the mapping process which causes a functional grouping of sections of the genotype
The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors
The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their func- tionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adapta- tions, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an oppor- tunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would sug- gest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to imple- ment novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engi- neering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engi- neering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms
Fault tolerant and dynamic evolutionary optimization engines
Mimicking natural evolution to solve hard optimization problems has played an important
role in the artificial intelligence arena. Such techniques are broadly classified
as Evolutionary Algorithms (EAs) and have been investigated for around four decades
during which important contributions and advances have been made.
One main evolutionary technique which has been widely investigated is the Genetic
Algorithm (GA). GAs are stochastic search techniques that follow the Darwinian
principle of evolution. Their application in the solution of hard optimization problems
has been very successful. Indeed multi-dimensional problems presenting difficult search
spaces with characteristics such as multi-modality, epistasis, non regularity, deceptiveness,
etc., have all been effectively tackled by GAs.
In this research, a competitive form of GAs known as fine or cellular GAs (cGAs)
are investigated, because of their suitability for System on Chip (SoC) implementation
when tackling real-time problems. Cellular GAs have also attracted the attention
of researchers due to their high performance, ease of implementation and massive
parallelism. In addition, cGAs inherently possess a number of structural configuration
parameters which make them capable of sustaining diversity during evolution and
therefore of promoting an adequate balance between exploitative and explorative stages
of the search.
The fast technological development of Integrated Circuits (ICs) has allowed a considerable
increase in compactness and therefore in density. As a result, it is nowadays
possible to have millions of gates and transistor based circuits in very small silicon
areas. Operational complexity has also significantly increased and consequently other
setbacks have emerged, such as the presence of faults that commonly appear in the
form of single or multiple bit flips. Tough environmental or time dependent operating
conditions can trigger faults in registers and memory allocations due to induced radiation, electron migration and dielectric breakdown. These kinds of faults are known as
Single Event Effects (SEEs).
Research has shown that an effective way of dealing with SEEs consists of a combination
of hardware and software mitigation techniques to overcome faulty scenarios.
Permanent faults known as Single Hard Errors (SHEs) and temporary faults known
as Single Event Upsets (SEUs) are common SEEs. This thesis aims to investigate the
inherent abilities of cellular GAs to deal with SHEs and SEUs at algorithmic level. A
hard real-time application is targeted: calculating the attitude parameters for navigation
in vehicles using Global Positioning System (GPS) technology. Faulty critical
data, which can cause a system’s functionality to fail, are evaluated. The proposed
mitigation techniques show cGAs ability to deal with up to 40% stuck at zero and 30%
stuck at one faults in chromosomes bits and fitness score cells.
Due to the non-deterministic nature of GAs, dynamic on-the-fly algorithmic and
parametric configuration has also attracted the attention of researchers. In this respect,
the structural properties of cellular GAs provide a valuable attribute to influence their
selection pressure. This helps to maintain an adequate exploitation-exploration tradeoff,
either from a pure topological perspective or through genetic operations that also
make use of structural characteristics in cGAs. These properties, unique to cGAs, are
further investigated in this thesis through a set of middle to high difficulty benchmark
problems. Experimental results show that the proposed dynamic techniques enhance
the overall performance of cGAs in most benchmark problems.
Finally, being structurally attached, the dimensionality of cellular GAs is another
line of investigation. 1D and 2D structures have normally been used to test cGAs at
algorithm and implementation levels. Although 3D-cGAs are an immediate extension,
not enough attention has been paid to them, and so a comparative study on the dimensionality
of cGAs is carried out. Having shorter radii, 3D-cGAs present a faster
dissemination of solutions and have denser neighbourhoods. Empirical results reported
in this thesis show that 3D-cGAs achieve better efficiency when solving multi-modal
and epistatic problems. In future, the performance improvements of 3D-cGAs will
merge with the latest benefits that 3D integration technology has demonstrated, such
as reductions in routing length, in interconnection delays and in power consumption
Biologically inspired computational structures and processes for autonomous agents and robots
Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Evolving Fault Tolerant Robotic Controllers
Fault tolerant control and evolutionary algorithms are two different research areas. However with the development of artificial intelligence, evolutionary algorithms have demonstrated competitive performance compared to traditional approaches for the optimisation task. For this reason, the combination of fault tolerant control and evolutionary algorithms has become a new research topic with the evolving of controllers so as to achieve different fault tolerant control schemes.
However most of the controller evolution tasks are based on the optimisation of controller parameters so as to achieve the fault tolerant control, so structure optimisation based evolutionary algorithm approaches have not been investigated as the same level as parameter optimisation approaches. For this reason, this thesis investigates whether structure optimisation based evolutionary algorithm approaches could be implemented into a robot sensor fault tolerant control scheme based on the phototaxis task in addition to just parameter optimisation, and explores whether controller structure optimisation could demonstrate potential benefit in a greater degree than just controller parameter optimisation.
This thesis presents a new multi-objective optimisation algorithm in the structure optimisation level called Multi-objective Cartesian Genetic Programming, which is created based on Cartesian Genetic Programming and Non-dominated Sorting Genetic Algorithm 2, in terms of NeuroEvolution based robotic controller optimisation. In order to solve two main problems during the algorithm development, this thesis investigates the benefit of genetic redundancy as well as preserving neutral genetic drift in order to solve the random neighbour pick problem during crowding fill for survival selection and investigates how hyper-volume indicator is employed to measure the multi-objective optimisation algorithm performance in order to assess the convergence for Multi-objective Cartesian Genetic Programming.
Furthermore, this thesis compares Multi-objective Cartesian Genetic Programming with Non-dominated Sorting Genetic Algorithm 2 for their evolution performance and investigates how Multi-objective Cartesian Genetic Programming could be performing for a more difficult fault tolerant control scenario besides the basic one, which further demonstrates the benefit of utilising structure optimisation based evolutionary algorithm approach for robotic fault tolerant control
Adaptive Search and Constraint Optimisation in Engineering Design
The dissertation presents the investigation and development of novel adaptive
computational techniques that provide a high level of performance when searching
complex high-dimensional design spaces characterised by heavy non-linear constraint
requirements. The objective is to develop a set of adaptive search engines that will allow
the successful negotiation of such spaces to provide the design engineer with feasible high
performance solutions.
Constraint optimisation currently presents a major problem to the engineering designer and
many attempts to utilise adaptive search techniques whilst overcoming these problems are
in evidence. The most widely used method (which is also the most general) is to
incorporate the constraints in the objective function and then use methods for
unconstrained search. The engineer must develop and adjust an appropriate penalty
function. There is no general solution to this problem neither in classical numerical
optimisation nor in evolutionary computation. Some recent theoretical evidence suggests
that the problem can only be solved by incorporating a priori knowledge into the search
engine.
Therefore, it becomes obvious that there is a need to classify constrained optimisation
problems according to the degree of available or utilised knowledge and to develop search
techniques applicable at each stage. The contribution of this thesis is to provide such a
view of constrained optimisation, starting from problems that handle the constraints on the
representation level, going through problems that have explicitly defined constraints (i.e.,
an easily computed closed form like a solvable equation), and ending with heavily
constrained problems with implicitly defined constraints (incorporated into a single
simulation model). At each stage we develop applicable adaptive search techniques that
optimally exploit the degree of available a priori knowledge thus providing excellent
quality of results and high performance. The proposed techniques are tested using both well
known test beds and real world engineering design problems provided by industry.British Aerospace,
Rolls Royce and Associate
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