الفهرس 
PROBLEM DEFINITIONOnly 14 pages are availabe for public view
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Abstract
Evolutionary algorithms are computer programs used to solve large and complex problems by simulating biological evolution. In EAs a number of artificial individuals distributed over search space, they compete continually with each other to discover most promising areas containing better solutions. EAs are more likely to find global optimum, since they search the optimum from multiple directions simultaneously. Another benefit is that they don’t require prior knowledge about the problem to optimize [1]. On the other side EAs don’t guarantee to find global optimum with every run due to stochastic search mechanism. Later researches on merging quantum computing principles with evolutionary algorithms have appeared under the name of quantum inspired evolutionary algorithms (QIEA) which adds behavior of heuristic quantum systems to the working methodology of EAs to find near optimum solutions.
In compare to classical computers that deal with binary digits (bits), quantum computers based on manipulating quantum bits (q-bits), Single q-bit represents two states (0 and 1) simultaneously. A quantum system | ⟩ with n q-bits represents states simultaneously; this is known as superposition feature of quantum system. Q-bits can interfere with each other to strengthen or form new states; this is known as interference mechanism of quantum system. The state of q-bit can be modified by intelligent quantum gates such as NOT gate, AND gate, OR gate and rotational gate. The original work of QIEA done by (Narayanan and Moore 1996) to solve traveling salesman problem [2] where the concept of quantum interference applied to crossover operator. QIEA attained more interest after the work done by (Han and Kim 2000) to solve knapsack problem [3] where solution individuals encoded in the form of quantum bits (q-bits).
Solving Non-linear programming problems by ordinary EAs face some challenges such as, sticking on local minimum that is spurious solutions, inability to find feasible solutions for nonlinear constraints problems and inconsistency.