Abstract
The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.
Highlights
The last few years have seen an exponential increase in the size of genomic databases cataloging the genetic basis of various diseases
The challenge in solving a global optimization problem is in seeking the global optimum rather than becoming trapped in a local optimum, an issue that will be addressed in more detail later[6]
A genetic algorithms (GAs) with less randomness leads to faster convergence towards local optimums; by limiting randomness it limits the search space, which in turns hinders the search for the global optimum
Summary
The last few years have seen an exponential increase in the size of genomic databases cataloging the genetic basis of various diseases. It has become critical to develop clever algorithms to reduce the time needed to search these databases and arrive at solutions to the treatment of genetically determined diseases[1]. Within a given search space S on the optimization function f, a global (absolute) optimum is sought This may take the form of a global maximum or minimum, depending on the original problem. The challenge in solving a global optimization problem is in seeking the global optimum rather than becoming trapped in a local optimum, an issue that will be addressed in more detail later[6]. These optimization problems can be approached with a variety of techniques. One popular technique is the use of GAs, the focus of this study[7]
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