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We give test cases and connectors a unifying formal semantics by using the notion of design in UTP, and generate test cases by solving constraints obtained from a specification and a faulty implementation.
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The simulation used shows how an algorithm for solving constraint satisfaction problems works.
In recent years, many new backtracking algorithms for solving constraint satisfaction problems have been proposed.
The main advantages of the proposed filter are its excellent stability properties and ease of implementation in standard softwares without Hessians or solving constraint systems.
Recently, an improved constrainted differential evolution (ICDE) has been proposed and proven to be robust and effective for solving constrainted optimization problems.
It can also explore trillions of possibilities to solve constraints like travel time, distance and closing hours.
Weighted CSP (Constraint Satisfaction Problems) are used to model and to solve constraint optimization problems.
Our method is implemented on top of the Gecode library (http://www.gecode.org), an efficient framework to solve constraint satisfaction problems [ 22, 23].
Our work in other domains has shown that the more specific sense of system memory demonstrated in the current paper can improve the ability of an adaptive network [ 32] to solve constraint problems or optimise resource allocation problems (without an external reward signal) [ 58].
A distributed search algorithm for solving Distributed Constraints Optimization Problems (DCOPs) is presented.
A WDS analysis program EPANET 2.0 is linked the optimal model for solving hydraulic constraints.
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