Annual report 2006 - North Pacific Marine Science Organization (PICES). Fifteenth meeting, Yokohama, Japan, October 13-22, 2006

◾ Report of Opening Session (p. 1) ◾ Report of Governing Council (p. 15) ◾ Report of the Finance and Administration Committee (p. 47) ◾ Reports of Science Board and Committees: Science Board Inter-sessional Meeting (p. 63); Science Board (p. 73); Biological Oceanography Committee (p. 87); Fishery Science Committee (p. 95); Marine Environmental Quality Committee (p. 105); MONITOR Technical Committee (p. 115); Physical Oceanography and Climate Committee (p. 125); Technical Committee on Data Exchange (p. 133) ◾ Reports of Sections, Working and Study Groups: Section on Carbon and Climate (p. 139); Section on Ecology of Harmful Algal Blooms in the North Pacific (p. 143); Working Group 18 on Mariculture in the 21st Century - The Intersection Between Ecology, Socio-economics and Production (p. 147); Working Group 19 on Ecosystem-Based Management Science and its Application to the North Pacific (p. 151); Working Group 20 on Evaluations of Climate Change Projections (p. 157); Working Group 21 on Non-indigenous Aquatic Species (p. 159); Study Group to Develop a Strategy for GOOS (p. 165) ◾ Reports of the Climate Change and Carrying Capacity Scientific Program: Implementation Panel on the CCCC Program (p. 169); CFAME Task Team (p. 175); MODEL Task Team (p. 181) ◾ Reports of Advisory Panels: Advisory Panel for a CREAMS/PICES Program in East Asian Marginal Seas (p. 187); Advisory Panel on Continuous Plankton Recorder Survey in the North Pacific (p. 193); Advisory Panel on Iron Fertilization Experiment in the Subarctic Pacific Ocean (p. 197); Advisory Panel on Marine Birds and Mammals (p. 201); Advisory Panel on Micronekton Sampling Inter-calibration Experiment (p. 205) ◾ Summary of Scientific Sessions and Workshops (p. 209) ◾ Membership List (p. 259) ◾ List of Participants (p. 277) ◾ List of PICES Acronyms (p. 301) ◾ List of Acronyms (p. 303)

Muitiobjective pressurized water reactor reload core design by nondominated genetic algorithm search

The design of pressurized water reactor reload cores is not only a formidable optimization problem but also, in many instances, a multiobjective problem. A genetic algorithm (GA) designed to perform true multiobjective optimization on such problems is described. Genetic algorithms simulate natural evolution. They differ from most optimization techniques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sense) solutions as parents more often, the population can be evolved to reveal the trade-off surface between the competing objectives. An example illustrating the effectiveness of this novel method is presented and analyzed. It is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends: on the core simulator used; the GA itself is code independent.