The siting of locally obnoxious but nationally relevant and necessary facilities such as sewage treatment plants, landfills, dams and nuclear power stations is an important issue in public policy planning. In view of the negative externalities such as declining property prices, health threats, and air, water and noise pollution imposed on the local communities that house them, the location of these facilities generates a consensus among the general public aptly termed 'not-in-my-backyard' or NIMBY syndrome. Drawing on the experiences of North America, Europe, Oceania and Asia, this book offers a comprehensive review of existing conflict-resolution instruments used in the siting of these facilities. The authors highlight in particular legal and command instruments such as zoning and compulsory acquisition of land, and economic incentives such as compensation and mitigation. Using elements from areas such as game theory and risk analysis and the use of compensation auction mechanisms, the authors present a series of decision steps to provide a credible alternative methodology designed to minimise such conflicts. This innovative study will be welcomed by all those with an interest in environmental and public policy planning.

This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning, held December 7-10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world