本文選題：遺傳算法 + 交互��； 參考：《中國礦業(yè)大學(xué)》2009年博士論文

【摘要】： 交互式遺傳算法把人的智慧和遺傳算法結(jié)合起來,主要用于解決無法建立顯式函數(shù)的隱式性能指標(biāo)優(yōu)化問題。交互式遺傳算法在發(fā)揮人類智慧的同時,也需要面對人自身的局限性。人的認(rèn)知局限性和易疲勞特點,使得交互式遺傳算法的種群規(guī)模較小和進(jìn)化代數(shù)較少,這限制了交互式遺傳算法的優(yōu)化性能。許多學(xué)者研究了改進(jìn)交互式遺傳算法性能的方法,這些方法幾乎都與用戶偏好信息相關(guān)。由于用戶偏好信息往往綜合了多種用戶認(rèn)知規(guī)律,因此,為了更好地獲取用戶偏好信息,必須深入研究交互式遺傳算法中用戶的認(rèn)知規(guī)律。但是,已有研究成果中對用戶認(rèn)知規(guī)律的研究卻很少。本文通過研究交互式遺傳算法中用戶的認(rèn)知規(guī)律,進(jìn)而研究交互式遺傳算法收斂理論和性能改進(jìn)方法。 本文內(nèi)容主要從以下5個方面展開:(1)研究交互式遺傳算法中用戶的參照認(rèn)知規(guī)律,分別考慮理論參照認(rèn)知和實際參照認(rèn)知的算法收斂理論,提出交互式遺傳算法全局收斂的強(qiáng)條件和弱條件;(2)研究交互式遺傳算法中用戶的理性認(rèn)知規(guī)律,提出用戶保持理性是交互式遺傳算法全局收斂的充分條件,并針對賦予適應(yīng)值的不同方法給出用戶保持理性的最大進(jìn)化代數(shù)估計;(3)研究交互式遺傳算法中用戶的不確定性認(rèn)知規(guī)律,給出用戶偏好知識提取、表示及更新方法,并結(jié)合定向變異,提出了改進(jìn)算法性能的方法;(4)研究交互式遺傳算法中用戶的選擇性注意認(rèn)知規(guī)律,提出獲取用戶選擇性注意的種群初始化方法和跟蹤用戶選擇性注意的個體生成方法,并給合用戶選擇性注意知識,提出算法性能改進(jìn)的方法;(5)研究交互式遺傳算法系統(tǒng)的實現(xiàn),給出交互式遺傳算法的系統(tǒng)實現(xiàn)框架、模塊劃分,并給出基于交互式遺傳算法的三維動漫人物造型系統(tǒng)。 本文的研究成果不僅豐富了交互式遺傳算法的基礎(chǔ)理論,而且為把交互式遺傳算法應(yīng)用于工程實踐提供了理論指導(dǎo)。
[Abstract]:Interactive genetic algorithm combines human intelligence with genetic algorithm, which is mainly used to solve the problem of implicit performance index optimization which can not establish explicit function. Interactive genetic algorithm not only exerts human intelligence, but also faces human limitations. Due to the cognitive limitation and fatigue, the interactive genetic algorithm has smaller population size and less evolutionary algebra, which limits the optimization performance of the interactive genetic algorithm. Many scholars have studied methods to improve the performance of interactive genetic algorithms, which are almost related to user preference information. Because user preference information often synthesizes a variety of user cognitive laws, in order to obtain user preference information better, it is necessary to deeply study the cognitive law of users in interactive genetic algorithm (IGA). However, there are few researches on the law of user cognition in the existing research results. In this paper, the convergence theory and performance improvement method of interactive genetic algorithm are studied by studying the cognitive law of users in interactive genetic algorithm. The main contents of this paper are as follows: (1) studying the rules of user's reference cognition in interactive genetic algorithm, considering the convergence theory of theoretical reference cognition and practical reference cognition, respectively. The strong and weak conditions for the global convergence of interactive genetic algorithms are proposed. (2) the rational cognitive laws of users in interactive genetic algorithms are studied, and the sufficient conditions for global convergence of interactive genetic algorithms are proposed. The maximum evolutionary algebraic estimation of user's rationality is given according to the different methods of endowing fitness. (3) the uncertain cognition law of user in interactive genetic algorithm is studied, and the method of extracting, expressing and updating user's preference knowledge is given. Combined with directional mutation, a method to improve the performance of the algorithm is proposed. (4) the rules of selective attention cognition of users in interactive genetic algorithm are studied. This paper proposes a population initialization method for obtaining user selective attention and an individual generation method for tracking user selective attention, and proposes a method to improve the performance of the algorithm by combining the knowledge of user selective attention. (5) the realization of interactive genetic algorithm system is studied. The system implementation framework and module partition of interactive genetic algorithm are presented, and the 3D animation character modeling system based on interactive genetic algorithm is presented. The research results of this paper not only enrich the basic theory of interactive genetic algorithm, but also provide theoretical guidance for the application of interactive genetic algorithm in engineering practice.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2009
【分類號】：TP18

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 王之元;DICOM醫(yī)學(xué)影像自適應(yīng)顯示技術(shù)的研究與實現(xiàn)[D];內(nèi)蒙古科技大學(xué);2013年

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本文編號：2086601