The length of a long review article, this short book describes the details of a recently developed soft computing (that is, a bio-inspired metaheuristic) algorithm, tested for numerical optimization and proven to be powerful after careful implementation.

The framework for the algorithm uses multiple so-called reproduction functions to generate new solutions, which aims for higher efficiency in exploration and exploitation. Reproduction means moving forward with new solutions, using materials for some existing solutions in the literature of bio-inspired algorithms. For instance, crossover and mutation are two prominent bio-inspired reproduction functions in evolutionary commutation. Similarly, the authors propose an algorithm that uses three reproduction functions for the same purpose: pollination, graft, and clone.

The provided details for implementation seem substantial and useful for solving different types of optimization problems. The test results are also satisfactory and show how the algorithm can be used for solving various kinds of numerical optimization problems. However, the algorithm needs to be comprehensively tested for solving combinatorial optimization problems, which seems to be missing from the test cases. A longer book on this algorithm would be more beneficial for implementations and problem solving.