Food and beverages rich in polyphenols have been shown to reduce the risk of non-communicable diseases. The present study estimated polyphenol levels and consumption from food and beverages in Japanese women. Randomly recruited housewives living in the area around Tokyo (n 109; aged 21–56 years; Group 1) recorded all beverages and foods they ingested for 7 d, and the total polyphenol (TP) consumption was estimated based on the TP content of each item measured with a modified Folin–Ciocalteu method. For Group 1, TP was consumed at 841 (sd 403) mg/d (range 113–1759 mg/d), and beverages were a larger source of TP (79 %) than food (21 %). The largest single source of TP was coffee at 47 %, followed by green tea, black tea, chocolate, beer and soya sauce, at 16, 5·7, 3·3, 3·2 and 3·1 %, respectively. In terms of food groups, cereals/noodles, vegetables, fruits, beans and seeds, and seasonings (except for soya sauce) contributed 5·0, 4·0, 1·4, 1·8 and 2·4 %, respectively. Another group of housewives who consumed at least one cup of coffee per d were separately recruited (n 100; Group 2) in the same area. Their consumption of TP was higher at 1187 (sd 371) mg/d (range 440–2435 mg/d) than Group 1 (P < 0·001), and the difference mostly came from the coffee consumption. We conclude that not food but beverages, especially coffee, may be the major contributor to TP consumption in Japanese women.
Recently it has been pointed out in many studies that evolutionary multi-objective optimization (EMO) algorithms with Pareto dominance-based fitness evaluation do not work well on many-objective problems with four or more objectives. In this paper, we examine the behavior of well-known and frequentlyused EMO algorithms such as NSGA-II, SPEA2 and MOEA/D on many-objective problems with correlated or dependent objectives. First we show that good results on many-objective 0/1 knapsack problems with randomly generated objectives are not obtained by Pareto dominance-based EMO algorithms (i.e., NSGA-II and SPEA2). Next we show that the search ability of NSGA-II and SPEA2 is not degraded by the increase in the number of objectives when they are highly correlated or dependent. In this case, the performance of MOEA/D is deteriorated. As a result, NSGA-II and SPEA2 outperform MOEA/D with respect to the convergence of solutions toward the Pareto front for some manyobjective problems. Finally we show that the addition of highly correlated or dependent objectives can improve the performance of EMO algorithms on two-objective problems in some cases.
Abstract.Recently MOEA/D (multi-objective evolutionary algorithm based on decomposition) was proposed as a high-performance EMO (evolutionary multiobjective optimization) algorithm. MOEA/D has high search ability as well as high computational efficiency. Whereas other EMO algorithms usually do not work well on many-objective problems with four or more objectives, MOEA/D can properly handle them. This is because its scalarizing function-based fitness evaluation scheme can generate an appropriate selection pressure toward the Pareto front without severely increasing the computation load. MOEA/D can also search for well-distributed solutions along the Pareto front using a number of weight vectors with different directions in scalarizing functions. Currently MOEA/D seems to be one of the best choices for multi-objective optimization in various application fields. In this paper, we examine its performance on multi-objective problems with highly correlated objectives. Similar objectives to existing ones are added to two-objective test problems in computational experiments. Experimental results on multi-objective knapsack problems show that the inclusion of similar objectives severely degrades the performance of MOEA/D while it has almost no negative effects on NSGA-II and SPEA2. We also visually examine such an undesirable behavior of MOEA/D using manyobjective test problems with two decision variables.
The iterated prisoner's dilemma (IPD) game has been frequently used to examine the evolution of cooperative behavior among agents in the field of evolutionary computation. A number of factors are known to be related to the evolution of cooperative behavior. One well-known factor is spatial relations among agents. The IPD game is often played in a grid-world. Such a spatial IPD game has a neighborhood structure which is used for local opponent selection in the IPD game and local parent selection in genetic operations. Another important factor is the choice of a representation scheme to encode each strategy. Different representation schemes often lead to totally different results. Whereas the choice of a representation scheme is known to be important, a mixture of different representation schemes has not been examined for the spatial IPD game in the literature. This means that a population of homogeneous agents with the same representation scheme has been assumed. In this paper, we introduce a different situation to the spatial IPD game in order to examine the evolution of cooperative behavior under more general assumptions. The main novelty of our spatial IPD game is the use of a mixture of different representation schemes. This means that we use a population of inhomogeneous agents with different representation schemes. Another novelty is the use of two neighborhood structures, each of which is used for local opponent selection and local parent selection. Under these specifications, we show a number of interesting observations on the evolution of cooperative behavior.
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