342,346 research outputs found
Mining protein database using machine learning techniques
With a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous. We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies. In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a "knowledge gap" in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins
Evolutionary algorithms in artificial intelligence: a comparative study through applications
For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system
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
Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms
Evolutionary artificial neural networks (EANNs) refer to a special class of
artificial neural networks (ANNs) in which evolution is another fundamental
form of adaptation in addition to learning. Evolutionary algorithms are used to
adapt the connection weights, network architecture and learning algorithms
according to the problem environment. Even though evolutionary algorithms are
well known as efficient global search algorithms, very often they miss the best
local solutions in the complex solution space. In this paper, we propose a
hybrid meta-heuristic learning approach combining evolutionary learning and
local search methods (using 1st and 2nd order error information) to improve the
learning and faster convergence obtained using a direct evolutionary approach.
The proposed technique is tested on three different chaotic time series and the
test results are compared with some popular neuro-fuzzy systems and a recently
developed cutting angle method of global optimization. Empirical results reveal
that the proposed technique is efficient in spite of the computational
complexity
Learning in evolutionary environments
Not availabl
Evolutionary Algorithms for Reinforcement Learning
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications
- …