The use of Evolutionary Algorithms (EA) for solving Mathematical/Computational Optimization Problems is inspired by the biological processes of Evolution. Few of the primitives involved in the Evolutionary process/paradigm are selection of 'Fit' individuals (from a population sample) for retention, cloning, mutation, discarding, breeding, crossover etc. In the Evolutionary Algorithm abstraction, the individuals are deemed to be solution candidates to an Optimization problem and additional solution(/sets) are built by applying analogies to the above primitives (cloning, mutation etc.) by means of evaluating a 'Fitness' function/criterion. One such algorithm is Differential Evolution (DE) which can be used to compute the minima of functions such as the rastrigin function and rosenbrock function. This work is an attempt to study the result of applying the DE method on these functions with candidate individuals generated on classical Turing modeled computation and comparing the same with those on state of the art Quantum computation.The study benchmarks the convergence of these functions by varying the parameters initialized and reports timing, convergence, and resource utilization results.

翻译：使用进化算法（EA）求解数学/计算优化问题，灵感来源于生物进化过程。进化过程/范式涉及的部分原始操作包括从种群样本中选择"适应"个体进行保留、克隆、突变、丢弃、繁殖、交叉等。在进化算法抽象中，个体被视为优化问题的候选解，并通过评估"适应度"函数/准则，应用上述原始操作（克隆、突变等）的类比来构建额外的解（或解集）。差分进化（DE）是此类算法之一，可用于计算诸如Rastrigin函数和Rosenbrock函数等函数的最小值。本研究尝试分析将DE方法应用于这些函数的结果，其中候选个体基于经典图灵模型计算生成，并与基于前沿量子计算的结果进行比较。研究通过改变初始化参数对函数收敛性进行基准测试，并报告了时序、收敛性和资源利用情况。