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samples with noisy echoes and echoes of various magnitudes), terminating resistor, receiver gain, and the loop termination. This generic set of components is referred to as the “base set” and the automated optimization tends to push each component of this base set to near its optimum value. The optimization techniques are based on a biomimetic approach similar to that used to understand the principles of natural evolution. Preliminary results using such an approach demonstrated significant improvements over classic approaches and over the results obtained with commercial optimization tools. The effectiveness of the approaches is evidenced by their abilities to optimize a large number of design configurations of the base set of components. The optimization is carried out using a new branch's-eye type of characterization of the loop's performance using the peak-to-average power ratio (PAPR). The required new design components are included in the base set of components.
Ever since the Random Search was conceived in the 1950s, there has been great interest in the efficient use of this method. Specifically, the reader is well-served by first understanding that alternative GA algorithms such as the Stochastic Hill Climbing (SHC), Simulated Annealing (SA), Evolution Strategies, and Non-dominated Sorting Genetic Algorithms (NSGA) can be weakened by being over-constrained (i.e., hampered by the implementation of multiple specified constraints).
In recent years, the co-evolutionary nature of the traditional GA and the Exact-Nondominated Sorting Genetic Algorithm II (NSGA-II) algorithm has served to make use of the full potential of the GA algorithm. Rather than using a binary genetic operator, either the Stokastic Hill Climbing (SHC) or Simulated Annealing (SA) algorithm can be integrated with the GA to make better use of the full probability space.
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