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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

In evolutionary multi-objective optimization (EMO) algorithms, diversity maintenance has been mainly discussed in the objective space in order to search for uniformly distributed non-dominated solutions along the entire Pareto front. In this paper, we propose three types of many-objective test problems with multiple Pareto optimal regions in the decision space. One type has multiple equivalent Pareto optimal regions. Another type has different but somewhat similar Pareto optimal regions. The other type has Pareto and local Pareto optimal regions. Our many-objective test problems are generated by placing multiple polygons of the same or similar shapes in a decision space. The ith objective is the minimization of the distance from a solution to the nearest ith vertex over all polygons. Thus the number of objectives is the same as the number of vertices of the polygons. The number of equivalent or similar Pareto regions in the decision space is the same as the number of the polygons.

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 frequently been used to examine the evolution of cooperative behavior among agents. When the effect of representation schemes of IPD game strategies was examined, the same representation scheme was usually assigned to all agents. That is, in the literature, a population of homogeneous agents was usually used in computational experiments. In this article, we focus on a slightly different situation where every agent does not necessarily use the same representation scheme. That is, a population can be a mixture of heterogeneous agents with different representation schemes. In computational experiments, we used binary strings of different lengths (i.e., three-bit and five-bit strings) to represent IPD game strategies. We examined the evolution of cooperative behavior among heterogeneous agents in comparison with the case of homogeneous ones for the standard IPD game with typical payoff values of 0, 1, 3, and 5. Experimental results showed that the evolution of cooperative behavior was slowed down by the use of heterogeneous agents. It was also demonstrated that a faster evolution of cooperative behavior is achieved among majority agents than among minority ones in a heterogeneous population.