NFSBST Framework Meta-heuristic perspective

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NFSBST Framework Meta-heuristic perspective by Mind Map: NFSBST Framework Meta-heuristic perspective

1. Genetic Improvement

1.1. MiniSAT Boolean Satisfiability solver

1.1.1. Energy Consumption

1.1.1.1. C: Connection between source code and the energy consumed during project compilation [P5].

1.1.1.1.1. AS: Simulation can partly address this issue.

1.1.1.2. C: Capturing the energy consumed outside the CPU proves to be challenging [P5].

1.1.1.2.1. AS: Simulation can partly address this issue.

1.2. Software systems

1.2.1. Energy Consumption

1.2.1.1. C: While Using the GIGI4 framework finding other factors that affect the energy consumption and their relative contribution is a challenge [P6].

1.2.1.1.1. AS: The energy consumption equation should be formulated inclusive of the parameters that determine the way the devices are used [P6].

1.2.2. Energy Consumption,Speed, Memory

1.2.2.1. C: Processor is highly susceptible to compiler generated errors [P7].

1.2.2.1.1. AS: Issue could be overcome by involving a penalty and then stopping the program from going to next iteration [P7]

1.2.2.2. C: Finding an effective fitness function by preserving the core functionality of the system is challenging [P7].

1.2.2.2.1. AS: Finding the right metrics and combining them effectively needs to be done to formulate an efficient fitness function.

1.3. Randomised algorithms

1.3.1. Self-stabilization time, fairness, service time

1.3.1.1. C: When using model checking verifying properties of larger systems is very expensive as the number of states grow exponentially with the problem size [P9].

1.3.1.1.1. AS: Always select a subset of the non-functional property set for the optimization

2. Genetic Algorithms

2.1. Cloud Systems(cloud service composition problem)

2.1.1. Performance, time, cost, reliability,

2.2. Service-oriented systems

2.2.1. Performance

2.3. Software Algorithms(Knapsack, travelling salesman, sorting problems

2.3.1. Algorithm performance.

3. Tabu Search

3.1. Mobile Applications

3.1.1. QoS/ Quality of Information

4. Multi-Objective Genetic algorithms

4.1. Systems developed using JAVA [p2]

4.1.1. Reliability, performance, Quality of Service (QoS)

4.1.1.1. T1: EvoChecker