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首页并行遗传算法库:提升囚徒困境求解效率
并行遗传算法库:提升囚徒困境求解效率
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"《通用并行遗传算法》是由Roderick Murphy撰写的一篇硕士论文,提交给都柏林大学的高性能计算数学系,以期获得计算机科学硕士学位。该论文于2003年10月提交,主要探讨了遗传算法在解决特定搜索和优化问题中的高效与有效性。遗传算法是一种广泛应用于复杂问题求解的强大工具,它依赖于一系列操作,如适应度函数评估、选择、交叉、变异以及可能的迁移等。 本文的核心贡献是提供了一个功能库,允许用户实现常见的遗传算法变体,这些操作可以针对具体问题进行定制。作者特别关注了并行化实现,采用Pthreads技术将主函数进行了优化。论文中以囚徒困境为例,对比了并行版本与纯串行实现的结果,结果显示并行遗传算法在解决此类问题时表现出显著的速度提升。 作者声明,这项工作没有作为学位作业在任何大学提交,所有成果均为作者独立完成,除非在文中明确标注。此外,图书馆在接受请求后会提供本作品的副本。论文最后的部分,包括了对研究工作的总结和致谢,以及对后续研究可能的影响和潜在应用领域的讨论。 这篇论文不仅介绍了遗传算法的基本原理,还展示了如何通过并行化技术将其优势扩展到多核系统,这对于处理大规模数据或计算密集型问题的现代计算环境具有实际价值。对于那些对遗传算法感兴趣,尤其是寻求在并行计算环境中应用算法改进的读者来说,这是一份极具参考价值的研究资料。"
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bits.
We start by randomly generating n such strings. These are the candidate solutions to
the problem.
By decoding each string to some value x, calculate its fitness, f(x).Repeat the
following steps until n offspring (new population members) have been created.
� Select, with replacement (i.e. with the possibility of selecting them again), two
parents from the population with probability proportional to their fitness.
� With probability p
c
(“crossover probability”) swap the bits of the pair before
some randomly chosen point to produce two offspring.
� With probability p
m
(“mutation probability”) change the value of individual
bits of the offspring string.
� If the number of population members is odd one of the offspring can be
discarded.
Replace the old population with the n offspring. Calculate the fitness of the new
members and start the process again.
Each iteration of this process is called a generation. Fifty to 100 generations are
typically carried out to solve a problem using a GA and the entire set of generations is
called a run. The fittest member over the entire run is typically taken as the required
solution.
![](https://csdnimg.cn/release/download_crawler_static/89070989/bge.jpg)
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The are a number of details to be filled in with regard to each step of the algorithm as
well as the values such as the number of members in the population and the
probabilities of crossover and mutation. These details will be dealt with in the next
chapter.
1.4. Comparison between Biological and GA Terminology
Not surprisingly much of the language used by the GA community has its origins in
that used by biologists. Some of the analogies are somewhat strained, since GAs are
generally greatly simplified compared with the real world genetic processes.
All living organisms consist of cells with each cell containing the same set of one or
more chromosomes, or strings of DNA that serve as “a blueprint” for that individual
organism. The binary (or other) string used in the GAs described above can be
considered to be a chromosome, but since only individuals with a single string are
considered in most GAs, the chromosome is also the genotype (The genotype is the
name given to the total number of chromosomes of an organism, for example, the
human genotype is comprised of 23 pairs of chromosomes).
Each chromosome can be subdivided into genes, each of which, roughly speaking,
encode a particular trait in the organism (e.g. eye colour). Each of the possible values
a gene can take on is called an allele (e.g. blue, green, brown or hazel). The position
of a gene in the chromosome is called its locus.
![](https://csdnimg.cn/release/download_crawler_static/89070989/bgf.jpg)
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In GA terminology, if we consider a multi-variable problem, a gene can be considered
as the bits that encode a particular parameter and an allele, an allowable value that
parameter can have.
The organism or phenotype is the result produced by the expression of the genotype
within its environment. In GAs this will be a particular set of unknown parameters, or
an individual solution vector.
Organisms, such as humans, whose chromosomes are arranged in pairs, are called
diploid. Organisms whose chromosomes are unpaired are called haploid. Most
sexually reproducing species are diploid, however, for simplicity most GAs only
consider unpaired chromosomes.
In nature mutation occurs when single nucleotides (elementary molecules of DNA)
get changed when being copied from parent to offspring. In GAs mutation consists of
flipping binary digits at a randomly chosen locus in the chromosome.
1.5. Applications of Genetic Algorithms
The lists of fields and types of problems to which genetic algorithms have been
successfully applied are ever growing. The following are just a few examples:
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