我可以在JGAP中使用可变长度的染色体吗? [英] Can i have a variable length chromosome in JGAP?

问题描述

Im使用JGAP生成原理图的测试向量.通过将染色体的基因设置为位，可以最大程度地覆盖单个测试向量.现在我需要用最少的testvector获得100％的覆盖率.

如果我将每个基因设计为一个测试向量，则id需要根据基因的数目和总覆盖率来计算适应度函数，并且id还需要同时进化染色体长度和每个测试向量(基因)位..

甚至有可能具有可变长度的染色体吗?

这类任务是否有标准设计?

解决方案

听起来有点类似于车辆路径问题(VRP).在那里，解决方案通常被编码为列表列表.每个列表代表了车辆的旅行，它们一起代表了问题的解决方案.

我假设您可以用类似的方式对其进行编码.考虑您要访问的客户要涵盖的每个点，并将每辆车都视为"testvector".您想覆盖所有点(通常在VRP解决方案中要拜访所有客户)，但是要用最少数量的车辆(= testvectors)覆盖它们.

您的问题有哪些具体约束条件?我假设您在某种测试向量上可以涵盖的点上有某种限制.

Im using JGAP to generate testvectors for a schematic. I got maximum coverage of a single testvector by setting the genes of a chromosome to be bits . Now i need to get a 100% coverage with minimum number of testvectors.

If i design each gene to be a testvector, id need to calculate a fitness function based on the number of genes and total coverage and id also need to evolve both chromosome length and each testvectors(genes) bits ..

is it even possible to have a variable length chromosome?

Are there any standard designs for this type of tasks?

解决方案

Sounds a bit similar to the vehicle routing problem (VRP). There the solution is often encoded as a list of lists. Each list represents the tour for a vehicle and all together they represent a solution to the problem.

I assume you could encode it in a similar way. Consider each point that you want to cover as a customer that you want to visit and consider each vehicle to be a "testvector". You want to cover all points (as typically in a VRP solution you want to visit all customers), but you want to cover them with a minimum number of vehicles (=testvectors).

What are the specific constraints of your problem? I assume you have some kind of limit on which points you can cover in a certain testvector.

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