Codes of conduct and certification issues for shrimp farming: a review

The growing demand for fishery products from aquaculture, especially shrimp, led to fierce criticisms about the unsustainable production and socially exploitative management. The product demand is combined with enhanced consumer concern for food safety, and environmental and social issues. Additionally, there is increasing consu mer demand for information about the origin and nature of products they consume and the safety of all inputs. From the shrimp pond farmer to the retailer, there is a growing desire to meet or exceed these consumer expectations, and to be seen to be applying responsible management techniques in the development of truly sustainable shrimp production systems. These demands led to the development of codes for better aquaculture practices for the shrimp industry to ensure a sustainable, environmentally friendly and socially equitable way to produce shrimp and for the consumer to be assured healthy food. Shrimp certification was introduced to respond to public perceptions and market requirements and increase public and consumer confidence in the production practices and the product. Currently there are a growing number of standards, "Codes of Practice," and certification schemes. Proliferation of Codes of Practice and certification schemes used by governments and the private sector industry for sustainable shrimp farming poses a number of challenges. Shrimp producers and exporters in the developing world of ten struggle to adapt to new and changing rules as they try to bring their farm-raised shrimp to different overseas markets. Additionally, there is the risk that Codes of Practice and certification schemes could affect the competitive position of resource-poor shrimp farmers and prevent benefits from the price premium at tained through certification. There is an urgent need for more globally accepted standards and certification guidelines, especially for the small-scale shrimp farmers, to provide guidance, serve as a basis for improved harmonization, and facilitate mutual recognition and equivalence of certification schemes

A Memetic Pareto Evolutionary Approach to Artificial Neural Networks

Evolutionary Artificial Neural Networks (EANN) have been a focus of research in the areas of Evolutionary Algorithms (EA) and Artificial Neural Networks (ANN) for the last decade. In this paper, we present an EANN approach based on pareto multi-objective optimization and differential evolution augmented with local search. We call the approach Memetic Pareto Artificial Neural Networks (MPANN). We show empirically that MPANN is capable to overcome the slow training of traditional EANN with equivalent or better generalization

[Photograph 2012.201.B1398.0003]

Photograph used for a newspaper owned by the Oklahoma Publishing Company