本論文提出以文化基因演算法求解具時窗之團隊越野競賽問題。在解的初始 化以叢集的概念來產生擁有不同路徑的解。本篇產生初始解方法與 GRASP-ELS
[5] 所提出將點分區的初始化方法還要好。接著使用解路徑結合的方式來做交配 的流程來產生與目前最佳解相似的路徑。本論文將不可行解加入族群演化，並且 提出在解的比較時當兩者皆為可行解所比較的適應值以及當比較的解中有不可 行解時比較的適應值。由實驗上看到當族群有不可行解演化時可以增加解的效果
，並且比較適應值時不可行解計算懲罰值也會提高效果。另外，改善我們的初始 化在路徑數較少的時候族群多樣性會比較少的問題，在初始化挑選點加入的過程 中使用隨機挑選，實驗可以看到針對路徑數較少的問題結果可以改善許多。最後 藉由常見的區域搜尋搜尋法來改善解。在實驗上可以發現使用基因演算法可以減 少執行區域搜尋的次數，會比只做區域搜尋的效果還要好。我們的實驗在 304 個 問題集中更新了 7 個問題集的最佳解，且執行時間可以比其他演算法快一些。
本論文提出的交配方式可能還無法每次產生我們想要的目標路徑，所以能有 效結合我們想要的目標路徑的方法是一個研究方向。此外不可行解在於族群演化 過程中，存在的代數與族群包含的個數都不會很多，如果可以接受一定數目的不 可行解在族群中，可能有助於對解的改善。另外將不可行解修復至可行解後可以 找到更好的解也是一個研究的方向。在區域搜尋 VND 部分，目前使用的鄰域函
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式改善解效果不大，可以使用其他路徑操作，例如：2-Opt,Or-Opt,2-Opt*等，對各 個操作搜尋嘗試做不一樣的組合，找出有效的操作組合也是可以研究的問題。
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