Neuron - synapse level problem decomposition method for cooperative neuro - evolution of feedforward networks for time series prediction

A major concern in cooperative coevolution for neuro-evolution is the appropriate problem decomposition method that takes into account the architectural properties of the neural network. Decomposition to the synapse and neuron level has been proposed in the past that have their own strengths and limitations depending on the application problem. In this paper, a new problem decomposition method that combines neuron and synapse level is proposed for feedfoward networks and applied to time series prediction. The results show that the proposed approach has improved the results in selected benchmark data sets when compared to related methods. It also has promising performance when compared to other computational intelligence methods from the literature

Rainfall prediction using Artificial Neural Network in the South Pacific region

Rainfall prediction is one of the most important and at the same time challenging task. Meteorologists can predict weather patterns such as rainfall based on atmospheric parameters such as Humidity, Temperature, etc. This paper presents research on rainfall prediction based on historical dataset through neural network by training a network and testing it. Mean squared error (MSE) is used to generalize the performance of the model. Three different dataset, training algorithm and hidden layer setting is used for prediction. The results obtained reveals that ideally all three training algorithm is producing good results as MSE is closer to zero. Undoubtedly, neural network proves to be most appropriate technique for forecasting various weather phenomena such as rainfall. It can be further alluded that Bayesian Regularization tends to give lower MSE values compare to the other two training algorithms

Iteration split with Firefly Algorithm and Genetic Algorithm to solve multidimensional knapsack problems

When we talk about optimization, we mean to get the best or the optimal solutions from some set of available substitutes for the problems. If constraints are introduced in the problem, the feasible range would change. As we venture further in optimization, different types of problems need different approaches. One very common problem is combinatorial optimization problems. Combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects. In simple terms, finding optimal solutions from some set of available datasets of a problem. Multi Knapsack Problem (MKP) is NP-hard combinational optimization problem better known as the multi-constraint knapsack problem. It is one of the extensively studied problems in the field as it has a variety of real world problems associated with it. In this paper, the Firefly algorithm is used with the Genetic algorithm to solve the Multidimensional Knapsack Problem (MKP). By using the properties of flashing behavior of fireflies together with genetic evolution, some benchmark problems are solved. The results are compared with some work from the literature

Stepping ahead based hybridization of meta - heuristic model for solving global optimization problems

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis

Modified Neuron-Synapse level problem decomposition method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction

Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods from the literature

Combinational problem decomposition method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction

The breaking down of a particular problem through problem decomposition has enabled complex problems to be solved efficiently. The two major problem decomposition methods used in cooperative coevolution are synapse and neuron level. The combination of both the problem decomposition as a hybrid problem decomposition has been seen applied in time series prediction. The different problem decomposition methods applied at particular area of a network can share its strengths to solve the problem better, which forms the major motivation. In this paper, we are proposing a combination utilization of two hybrid problem decomposition method for Elman recurrent neural networks and applied to time series prediction. The results reveal that the proposed method has got better results in some datasets when compared to its standalone methods. The results are better in selected cases for proposed method when compared to several other approaches from the literature

Neuron - synapse level problem decomposition method for cooperative coevolution of recurrent networks for time series prediction

The decomposition of a particular problem can become a tiresome task if little connections between the elements is needed. In cooperative coevolution of recurrent networks, synapse and neuron level are the two noteworthy problem decomposition methods. Through combination of both of the problem decomposition methods, the individual problem decomposition methods can share its strengths to solve the problem at hand better. In this paper, a recently proposed problem decomposition method known as Neuron-Synapse problem decomposition method is modified for Elman recurrent neural networks. The results reveal that the proposed method has got better results in selected datasets when compared to standalone methods. The results are better in some cases for proposed method when compared to other approaches from the literature

A Preference-based Stepping ahead Artificial Bee Colony Algorithm for Global Optimization

Information systems are nowadays more inclined towards using Artificial Intelligence to improve and evolve. One area that provides the solution for improvement in terms of efficiency is optimization. Optimization problems are everywhere and the complexity is increasing, therefore, more intelligent approaches are needed. An intelligent approach that has shown promising results in solving real-world problems is the artificial bee colony (ABC) algorithm. In this paper, the ABC algorithm is modified with a novel technique called stepping ahead. The proposed method is called the preference-based stepping ahead Artificial Bee Colony (ABC-Step) Algorithm. The algorithm takes advantage of the best solution to find solutions more superior through better exploitation of the search space. The proposed algorithm is tested and validated on a set of 20 benchmark global optimization functions together with performance analysis on popular metaheuristics algorithms. ABC-Step provides competitive results to the compared algorithms from the literature and the ABC algorithm

Iteration split with Firefly Algorithm and Genetic Algorithm to solve multidimensional knapsack problems

When we talk about optimization, we mean to get the best or the optimal solutions from some set of available substitutes for the problems. If constraints are introduced in the problem, the feasible range would change. As we venture further in optimization, different types of problems need different approaches. One very common problem is combinatorial optimization problems. Combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects. In simple terms, finding optimal solutions from some set of available datasets of a problem. Multi Knapsack Problem (MKP) is NP-hard combinational optimization problem better known as the multi-constraint knapsack problem. It is one of the extensively studied problems in the field as it has a variety of real world problems associated with it. In this paper, the Firefly algorithm is used with the Genetic algorithm to solve the Multidimensional Knapsack Problem (MKP). By using the properties of flashing behavior of fireflies together with genetic evolution, some benchmark problems are solved. The results are compared with some work from the literature

Meta-heuristic approaches to tackle Skill Based Group allocation of Students in Project Based Learning Courses

In the arena of software engineering, Project Based Learning (PBL) is one of the fundamental components of practical based assessment. PBL involves team formation where necessary skills are needed to execute the project. Traditionally, the teams were randomly allocated based on individual preferences. To cab on this issue, preference based model needs few refinements such as skills needs to be identified by the facilitator while the students provide the necessary skill data. This way, students get assigned based on their skill rather than just random allocation. In a worst case scenario for random allocation, a team can end up with a very strong team having high skills or vice versa where a team has all of its members with limited skill or few skills are missing. The group created by skill preference would allow each group to more or less have the same strength and nearly all skills would be present in a group. In this paper, a method is extended from its original to cater for other state-of-the-art optimization techniques rather than just genetic algorithm to find a method that can suit small or large dataset. The objective function takes into account the differences between the total skill set of each group with the average total skill set needed for each group and the missing skill penalty of each group is added. Missing skill penalty is incurred due to not satisfying all the constraints such as non-presence of all the skills in a group. The skill rating allows better selection of members in a software engineering course. The results discussed in this paper are from 5 courses of one university