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Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework
Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA
Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation
A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real tim
Search Based Software Engineering in Membrane Computing
This paper presents a testing approach for kernel P Systems (kP systems),
based on test data generation for a given scenario. This method uses Genetic Algorithms
to generate the input sets needed to trigger the given computation steps
The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
Optimizing Engagement Simulations Through the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) Software
The ability to effectively model and simulate military missions holds the potential to save lives, money, and resources for the United States. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) software is a tool used to rapidly simulate and model new technologies and mission level scenarios. In this thesis, our objective is to integrate a closed loop optimization routine with AFSIM to identify an effective objective function to assess optimal inputs for engagement scenarios. Given the many factors which impact a mission level engagement, we developed a tool which interfaces with AFSIM to observe the effects from multiple inputs in an engagement scenario. Our tool operates under the assumption that simulation results have met an acceptable convergence threshold. The objective function evaluates the effectiveness and associated cost with a scenario using a genetic algorithm and a particle swarm optimization algorithm. From this, a statistical analysis was performed to assess risk from the distribution of effectiveness and cost at each point. The method allows an optimal set of inputs to be selected for a desired result from the selected engagement scenario.No embargoAcademic Major: Mechanical Engineerin
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