Catálogo de publicaciones - libros

Kotler and Trias De Bes (2003) at Lateral Marketing defined the creativity: each cluster had its own concepts, when a new need was generated and the designer could not find a solution from his own clusters, therefore he had a gap to overcome. This gap was as the original of creativity. If he wants to solve this problem, chose a new important concept for beginning was the only way he could do. This phenomenon was called the laterally transmitting. Then according to the designer’s subjective to choose the concept and connected other cluster to generate or enter a new cluster. This kind designing process could generate a creative product. But it also brought a new problem, there had many concepts in conceptual space, how to decide an effectiveness concept and extents it to create a good product. Here we combined Watt’s (2003) Small World model and Ohsawa and McBurney (2003) Chance Discovery Model to decide the creative probability and decreased the searching path length. Finally, we integrated the choosing mechanism and recombination mechanism into our chance based IEC (CBIEC) model. And we applied on the cell phone design. After the experiment we analyzed the interactive data found the choosing mechanism could bring the effective creativity and the recombination mechanism could quickly search as we expected the short-cut effect. Beside these results we also directly investigated the subjective of designer found our CBIEC model also better than the IGA (interactive genetic algorithms, Caldwell and Johnston, 1991).

In the standard GA, the individual has no intelligence and must act upon some rules established by a programmer in advance, such as various genetic operator. The result is to make the evolutionary process to be trapped into the local optimization of the objective function. In order to solve this problem, through studying the structure of an agent and selection operator, the paper designs a new genetic algorithm based on agent, called AGA (Agent-based Genetic Algorithm). At the premise of giving the definition of the outer environment where an agent lives and of an agent’s belief, this paper gives some rules on how an agent selects one agent to cross their genes and some rules on how to solve competition. In addition, a communication method based on blackboard is presented to solve the communication among the agent society. Finally, the paper gives the structure of AGA and the simulation result for a multi-peak function, which demonstrates the validity of the AGA.

This paper proposes an improved genetic algorithm for producing aesthetically pleasing drawings of general undirected graphs. Previous undirected graph drawing algorithms draw large cycles with no chords as concave polygons. In order to overcome such disadvantage, the genetic algorithm in this paper designs a new mutation operator single-vertex- neighborhood mutation and adds a component aiming at symmetric drawings to the fitness function, and it can draw such type graphs as convex polygons. The improved algorithm is of following advantages: The method is simple and it is easy to be implemented, and the drawings produced by the algorithm are beautiful, and also it is flexible in that the relative weights of the criteria can be altered. The experiment results show that the drawings of graphs produced by our algorithm are more beautiful than those produced by simple genetic algorithms, the original spring algorithm and the algorithm in bibliography [4].

In this paper, a novel niching approach to solve the multimodal function optimization problems is proposed. We firstly analyze and compare the characteristics and behaviors of a variety of niching methods as the fitness sharing, the crowding and deterministic crowding, the restricted mating, and the island model GA with regard to the competition, exploration & exploitation, genetic drift, and the ability to locate and maintain niches. Then we put forward the idea that the local competition of individuals is crucial to realize the distribution equilibria among niches of the optimization functions, and two types of niching methods, -nearest neighbor replacement and parental neighbor replacement, are formulated by adopting special replacement policies in the setting of the SSGA. Finally, we use a set of test functions to illustrate the efficacy and efficiency of the proposed methods and the DC scheme based on the SSGA.

In the research of MOEA (Multi-Objective Evolutionary Algorithm), many algorithms for multi-objective optimization have been proposed. Diversity of the solutions is an important measure, and it is also significant how to evaluate the diversity of an MOEA. In this paper, the clustering algorithm based on the distance between individuals is discussed, and a diversity metric based on clustering is also proposed. Applying this metric, we compare several popular multi-objective evolutionary algorithms. It is shown by experimental results that the method proposed in this paper performs well, especially helps to provide a comparative evaluation of two or more MOEAs.

Combinatorial auctions are efficient mechanisms for allocating resource in complex marketplace. Winner determination, which is NP-complete, is the core problem in combinatorial auctions. This paper proposes an immune partheno-genetic algorithm (IPGA) for solving this problem. Firstly, a zero-one programming model is built for the winner determination problem with XOR-bids and OR-bids. Then, steps of constructing three partheno-genetic operators and an immune operator are introduced. In the immune operation, new heuristics are designed for vaccines selection and vaccination. Simulation results show that the IPGA achieves good performance in large size problems and the immune operator can improve the searching ability and increase the converging speed greatly.

Enlightened by traditional Chinese medicine theory, a novel genetic algorithm (CMGA), which applies two types of treatment methods of “bu” and “xie” and dialectical treatment principle of traditional Chinese medicine theory to canonical GA, is proposed. The core of CMGA lies on constructing a cure operator, which is dynamically assembled with “bu” operation that replaces normal genes with eugenic genes and “xie” operation that replaces abnormal genes with normal genes. The main idea underlying CMGA is to give full play to the role of guidance function of knowledge to the evolutionary process through the cure operator. The simulation test of TSP shows that CMGA can restrain the degeneration and premature convergence phenomenon effectively during the evolutionary process while greatly increasing the convergence speed.

This paper presents a novel genetic algorithm (GA) for the scheduling problem of input-Queued switch, which can be applied in various networks besides the design of high speed routers. The scheduler should satisfy quality of service (QoS) constraints such as throughput and jitter. Solving the scheduling problem for the input-Queued switches can be divided into two steps: Firstly, decomposing the given rate matrix into a sum of permutation matrices with their corresponding weights; secondly, allocating the permutation matrices in one scheduling period based on their weights. It has been proved that scheduling problem in input-Queued switch with throughput and jitter constraints is NP-complete. The main contribution of this paper is a GA based algorithm to solve this NP-complete problem. We devise chromosome codes, fitness function, crossover and mutation operations for this specific problem. Experimental results show that our GA provides better performances in terms of throughput and jitter than a greedy heuristic.

A new approach to evolutionary computation with mutation only is developed by the introduction of the mutation matrix. The method of construction of the mutation matrix is problem independent and the selection mechanism is achieved implicitly by individualized and locus specific mutation probability based on the information on locus statistics and fitness of the population, and traditional genetic algorithm with selection and mutation can be treated a special case. The mutation matrix is parameter free and adaptive as the mutation probability is time dependent, and captures the accumulated information in the past generations. Three methodologies, mutation by row, mutation by column, and mutation by mixing row and column are introduced and tested on the resource allocation problem of the zero/one knapsack problem, showing high efficiency in speed and high quality of solution compared to other traditional methods.

This improved algorithm, Gray-encoded hybrid accelerating genetic algorithm (GHAGA), is presented to reduce computational amount and to improve the computational accuracy for the global optimization of water environmental models. The hybrid method combines two algorithms, which are the Gray-encoded genetic algorithm and Hooke-Jeeves algorithm. With the shrinking of searching range, the method gradually directs to optimal result with the excellent individuals obtained by Gray genetic algorithm embedding the Hooke-Jeeves searching operator. The convergence and global optimization of the new genetic algorithm are analyzed. Its global convergence rate is 100%, and the computational velocity is fast for five test functions. And it is efficient for the global optimization in the practical water environmental model on wastewater treatment.