Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and am optimization algorithm for feature selection.
Multi-object traveling salesman problem (MOTSP) is a typical multi-object optimization problem. It requires to select a best route and make a balance between cost assignment and distance assignment of the route, the less cost of the whole travel and to satisfy the stipulate is the guide line. This paper gives the non-domination of genetic algorithm, and shows a simple model to put out the method that using multi-object genetic algorithm to solve the TSP. The algorithm use integer coding method, create an initial population that satisfies the basic qualification; calculate the two objective-value: distance and cost; then rank the chromosomes with Pareto function according to the objective-value; and use tournament selection to select the better chromosomes to form a series of parents, through multi-objective greedy crossover, and then use transposition mutation algorithm; we can get a new population that forms of new individuals based on genetic-searching function, and get the approximately best solution at last. The computing results of real examples of the MOTSP demonstrates that the approximate global optimal solution of the problem can be quickly obtained, and the solution with high accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.