用随机键编码解决离散优化问题，提升算法通用性与可维护性。
Random-key genetic algorithms: Principles and applications
- 用[0,1)区间实数向量编码解，通过解码器映射到实际问题解
- 精英保留+变异+参数化均匀交叉，保持算法在单位超立方体中运行
- 适合各类离散优化问题，尤其适合需快速复用框架的场景
随机键遗传算法是一种用于离散与全局优化的进化元启发式方法。每个解以长度为N的随机键向量表示，其中每个随机键是[0,1)区间内随机生成的实数。通过解码器将随机键向量映射为具体优化问题的解并计算其代价。该方法的优势在于所有遗传算子和变换均可在单位超立方体内进行，不依赖具体问题，从而增强核心框架的效率与可维护性。算法初始种群包含P个随机键向量。每轮迭代中，向量被分为两组：高价值精英解集与非精英解集。所有精英个体直接复制至下一代。下一世代还加入少量随机键向量（突变体）。其余个体通过Spears和DeJong（1991）提出的参数化均匀交叉，成对组合生成。本文综述随机键遗传算法，并介绍一种有效变体——有偏随机键遗传算法。
原文摘要 · Abstract (English)
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regardless of the problem being addressed. This enhances the productivity and maintainability of the core framework. The algorithm starts with a population of P vectors of random keys. At each iteration, the vectors are partitioned into two sets: a smaller set of high-valued elite solutions and the remaining non-elite solutions. All elite elements are copied, without change, to the next population. A small number of random-key vectors (the mutants) is added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong (1991), pairs of solutions. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.
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