We are witness of the rapidly growing application of the Evolutionary Algorithms to solve real-world nonlinear complex problems. The computational time of these algorithms directly depends on the dimensionality and complexity of the problem. Although, compared to traditional methods, these algorithms are more applicable to real-world problems, they suffer from low convergence speed. So far, many attempts have been conducted to make them as fast as possible. In the current study, a new direction has been disclosed for this purpose, namely, employing the power of oppositeness to accelerate evolutionary algorithms. For the first time, the concept of opposition has been utilized in optimization. The opposition-based optimization is simple to implement and apply in many different ways for various purposes. This study is a starting point in this direction to confirm the potentials, and to motivate other researchers in optimization and machine learning fields to engage with the opposition concepts. This book is especially useful for researchers and professionals in soft computing, machine learning, or optimization.

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